Adipose Tissue Macrophage-Derived Exosomal miRNAs Can Modulate In Vivo and In Vitro Insulin Sensitivity.

MiRNAs are regulatory molecules that can be packaged into exosomes and secreted from cells. Here, we show that adipose tissue macrophages (ATMs) in obese mice secrete miRNA-containing exosomes (Exos), which cause glucose intolerance and insulin resistance when administered to lean mice. Conversely, ATM Exos obtained from lean mice improve glucose tolerance and insulin sensitivity when administered to obese recipients. miR-155 is one of the miRNAs overexpressed in obese ATM Exos, and earlier studies have shown that PPARγ is a miR-155 target. Our results show that miR-155KO animals are insulin sensitive and glucose tolerant compared to controls. Furthermore, transplantation of WT bone marrow into miR-155KO mice mitigated this phenotype. Taken together, these studies show that ATMs secrete exosomes containing miRNA cargo. These miRNAs can be transferred to insulin target cell types through mechanisms of paracrine or endocrine regulation with robust effects on cellular insulin action, in vivo insulin sensitivity, and overall glucose homeostasis.

Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission.

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing and/or sequencing capacity, which can also introduce biases1-3. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,5. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

Combinatorial CRISPR-Cas9 Metabolic Screens Reveal Critical Redox Control Points Dependent on the KEAP1-NRF2 Regulatory Axis.

The metabolic pathways fueling tumor growth have been well characterized, but the specific impact of transforming events on network topology and enzyme essentiality remains poorly understood. To this end, we performed combinatorial CRISPR-Cas9 screens on a set of 51 carbohydrate metabolism genes that represent glycolysis and the pentose phosphate pathway (PPP). This high-throughput methodology enabled systems-level interrogation of metabolic gene dispensability, interactions, and compensation across multiple cell types. The metabolic impact of specific combinatorial knockouts was validated using 13C and 2H isotope tracing, and these assays together revealed key nodes controlling redox homeostasis along the KEAP-NRF2 signaling axis. Specifically, targeting KEAP1 in combination with oxidative PPP genes mitigated the deleterious effects of these knockouts on growth rates. These results demonstrate how our integrated framework, combining genetic, transcriptomic, and flux measurements, can improve elucidation of metabolic network alterations and guide precision targeting of metabolic vulnerabilities based on tumor genetics.

Interleukin-18 signaling promotes activation of hepatic stellate cells in mouse liver fibrosis.

BACKGROUND AND AIMS: Nucleotide-binding oligomerization domain-like receptor-family pyrin domain-containing 3 (NLRP3) inflammasome activation has been shown to result in liver fibrosis. Mechanisms and downstream signaling remain incompletely understood. Here, we studied the role of IL-18 in hepatic stellate cells (HSCs), and its impact on liver fibrosis. APPROACH AND RESULTS: We observed significantly increased serum levels of IL-18 (128.4 pg/ml vs. 74.9 pg/ml) and IL-18 binding protein (BP; 46.50 ng/ml vs. 15.35 ng/ml) in patients with liver cirrhosis compared with healthy controls. Single cell RNA sequencing data showed that an immunoregulatory subset of murine HSCs highly expresses Il18 and Il18r1 . Treatment of cultured primary murine HSC with recombinant mouse IL-18 accelerated their transdifferentiation into myofibroblasts. In vivo , IL-18 receptor-deficient mice had reduced liver fibrosis in a model of fibrosis induced by HSC-specific NLRP3 overactivation. Whole liver RNA sequencing analysis from a murine model of severe NASH-induced fibrosis by feeding a choline-deficient, L-amino acid-defined, high fat diet showed that genes related to IL-18 and its downstream signaling were significantly upregulated, and Il18-/- mice receiving this diet for 10 weeks showed protection from fibrotic changes with decreased number of alpha smooth muscle actin-positive cells and collagen deposition. HSC activation triggered by NLRP3 inflammasome activation was abrogated when IL-18 signaling was blocked by its naturally occurring antagonist IL-18BP. Accordingly, we observed that the severe inflammatory phenotype associated with myeloid cell-specific NLRP3 gain-of-function was rescued by IL-18BP. CONCLUSIONS: Our study highlights the role of IL-18 in the development of liver fibrosis by its direct effect on HSC activation identifying IL-18 as a target to treat liver fibrosis.

Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions.

We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual guide RNAs in three cell lines, comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified, and these patterns replicated with combinatorial drugs at 75% precision. From these results, we anticipate that cellular context will be critical to synthetic-lethal therapies.

Efficient population-scale variant analysis and prioritization with VAPr.

Summary: With the growing availability of population-scale whole-exome and whole-genome sequencing, demand for reproducible, scalable variant analysis has spread within genomic research communities. To address this need, we introduce the Python package Variant Analysis and Prioritization (VAPr). VAPr leverages existing annotation tools ANNOVAR and MyVariant.info with MongoDB-based flexible storage and filtering functionality. It offers biologists and bioinformatics generalists easy-to-use and scalable analysis and prioritization of genomic variants from large cohort studies. Availability and implementation: VAPr is developed in Python and is available for free use and extension under the MIT License. An install package is available on PyPi at https://pypi.python.org/pypi/VAPr, while source code and extensive documentation are on GitHub at https://github.com/ucsd-ccbb/VAPr.

Interactive network visualization in Jupyter notebooks: visJS2jupyter.

Motivation: Network biology is widely used to elucidate mechanisms of disease and biological processes. The ability to interact with biological networks is important for hypothesis generation and to give researchers an intuitive understanding of the data. We present visJS2jupyter, a tool designed to embed interactive networks in Jupyter notebooks to streamline network analysis and to promote reproducible research. Results: The tool provides functions for performing and visualizing useful network operations in biology, including network overlap, network propagation around a focal set of genes, and co-localization of two sets of seed genes. visJS2jupyter uses the JavaScript library vis.js to create interactive networks displayed within Jupyter notebook cells with features including drag, click, hover, and zoom. We demonstrate the functionality of visJS2jupyter applied to a biological question, by creating a network propagation visualization to prioritize risk-related genes in autism. Availability and implementation: The visJS2jupyter package is distributed under the MIT License. The source code, documentation and installation instructions are freely available on GitHub at https://github.com/ucsd-ccbb/visJS2jupyter. The package can be downloaded at https://pypi.python.org/pypi/visJS2jupyter. Contact: sbrosenthal@ucsd.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

HIV-1 TAT protein enhances sensitization to methamphetamine by affecting dopaminergic function.

Methamphetamine abuse is common among humans with immunodeficiency virus (HIV). The HIV-1 regulatory protein TAT induces dysfunction of mesolimbic dopaminergic systems which may result in impaired reward processes and contribute to methamphetamine abuse. These studies investigated the impact of TAT expression on methamphetamine-induced locomotor sensitization, underlying changes in dopamine function and adenosine receptors in mesolimbic brain areas and neuroinflammation (microgliosis). Transgenic mice with doxycycline-induced TAT protein expression in the brain were tested for locomotor activity in response to repeated methamphetamine injections and methamphetamine challenge after a 7-day abstinence period. Dopamine function in the nucleus accumbens (Acb) was determined using high performance liquid chromatography. Expression of dopamine and/or adenosine A receptors (ADORA) in the Acb and caudate putamen (CPu) was assessed using RT-PCR and immunohistochemistry analyses. Microarrays with pathway analyses assessed dopamine and adenosine signaling in the CPu. Activity-dependent neurotransmitter switching of a reserve pool of non-dopaminergic neurons to a dopaminergic phenotype in the ventral tegmental area (VTA) was determined by immunohistochemistry and quantified with stereology. TAT expression enhanced methamphetamine-induced sensitization. TAT expression alone decreased striatal dopamine (D1, D2, D4, D5) and ADORA1A receptor expression, while increasing ADORA2A receptors expression. Moreover, TAT expression combined with methamphetamine exposure was associated with increased adenosine A receptors (ADORA1A) expression and increased recruitment of dopamine neurons in the VTA. TAT expression and methamphetamine exposure induced microglia activation with the largest effect after combined exposure. Our findings suggest that dopamine-adenosine receptor interactions and reserve pool neuronal recruitment may represent potential targets to develop new treatments for methamphetamine abuse in individuals with HIV.

Advances in CRISPR-Cas9 genome engineering: lessons learned from RNA interference.

The discovery that the machinery of the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 bacterial immune system can be re-purposed to easily create deletions, insertions and replacements in the mammalian genome has revolutionized the field of genome engineering and re-invigorated the field of gene therapy. Many parallels have been drawn between the newly discovered CRISPR-Cas9 system and the RNA interference (RNAi) pathway in terms of their utility for understanding and interrogating gene function in mammalian cells. Given this similarity, the CRISPR-Cas9 field stands to benefit immensely from lessons learned during the development of RNAi technology. We examine how the history of RNAi can inform today's challenges in CRISPR-Cas9 genome engineering such as efficiency, specificity, high-throughput screening and delivery for in vivo and therapeutic applications.

Sharing and archiving nucleic acid structure mapping data.

Nucleic acids are particularly amenable to structural characterization using chemical and enzymatic probes. Each individual structure mapping experiment reveals specific information about the structure and/or dynamics of the nucleic acid. Currently, there is no simple approach for making these data publically available in a standardized format. We therefore developed a standard for reporting the results of single nucleotide resolution nucleic acid structure mapping experiments, or SNRNASMs. We propose a schema for sharing nucleic acid chemical probing data that uses generic public servers for storing, retrieving, and searching the data. We have also developed a consistent nomenclature (ontology) within the Ontology of Biomedical Investigations (OBI), which provides unique identifiers (termed persistent URLs, or PURLs) for classifying the data. Links to standardized data sets shared using our proposed format along with a tutorial and links to templates can be found at http://snrnasm.bio.unc.edu.

Wastewater and surface monitoring to detect COVID-19 in elementary school settings: The Safer at School Early Alert project.

Background: Schools are high-risk settings for SARS-CoV-2 transmission, but necessary for children's educational and social-emotional wellbeing. Previous research suggests that wastewater monitoring can detect SARS-CoV-2 infections in controlled residential settings with high levels of accuracy. However, its effective accuracy, cost, and feasibility in non-residential community settings is unknown. Methods: The objective of this study was to determine the effectiveness and accuracy of community-based passive wastewater and surface (environmental) surveillance to detect SARS-CoV-2 infection in neighborhood schools compared to weekly diagnostic (PCR) testing. We implemented an environmental surveillance system in nine elementary schools with 1700 regularly present staff and students in southern California. The system was validated from November 2020 - March 2021. Findings: In 447 data collection days across the nine sites 89 individuals tested positive for COVID-19, and SARS-CoV-2 was detected in 374 surface samples and 133 wastewater samples. Ninety-three percent of identified cases were associated with an environmental sample (95% CI: 88% - 98%); 67% were associated with a positive wastewater sample (95% CI: 57% - 77%), and 40% were associated with a positive surface sample (95% CI: 29% - 52%). The techniques we utilized allowed for near-complete genomic sequencing of wastewater and surface samples. Interpretation: Passive environmental surveillance can detect the presence of COVID-19 cases in non-residential community school settings with a high degree of accuracy. Funding: County of San Diego, Health and Human Services Agency, National Institutes of Health, National Science Foundation, Centers for Disease Control.

Safer at school early alert: an observational study of wastewater and surface monitoring to detect COVID-19 in elementary schools.

Background: Schools are high-risk settings for SARS-CoV-2 transmission, but necessary for children's educational and social-emotional wellbeing. Previous research suggests that wastewater monitoring can detect SARS-CoV-2 infections in controlled residential settings with high levels of accuracy. However, its effective accuracy, cost, and feasibility in non-residential community settings is unknown. Methods: The objective of this study was to determine the effectiveness and accuracy of community-based passive wastewater and surface (environmental) surveillance to detect SARS-CoV-2 infection in neighborhood schools compared to weekly diagnostic (PCR) testing. We implemented an environmental surveillance system in nine elementary schools with 1700 regularly present staff and students in southern California. The system was validated from November 2020 to March 2021. Findings: In 447 data collection days across the nine sites 89 individuals tested positive for COVID-19, and SARS-CoV-2 was detected in 374 surface samples and 133 wastewater samples. Ninety-three percent of identified cases were associated with an environmental sample (95% CI: 88%-98%); 67% were associated with a positive wastewater sample (95% CI: 57%-77%), and 40% were associated with a positive surface sample (95% CI: 29%-52%). The techniques we utilized allowed for near-complete genomic sequencing of wastewater and surface samples. Interpretation: Passive environmental surveillance can detect the presence of COVID-19 cases in non-residential community school settings with a high degree of accuracy. Funding: County of San Diego, Health and Human Services Agency, National Institutes of Health, National Science Foundation, Centers for Disease Control.

Statistical methods for analysis of high-throughput RNA interference screens.

RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however, small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow.

The ViReflow pipeline enables user friendly large scale viral consensus genome reconstruction.

Throughout the COVID-19 pandemic, massive sequencing and data sharing efforts enabled the real-time surveillance of novel SARS-CoV-2 strains throughout the world, the results of which provided public health officials with actionable information to prevent the spread of the virus. However, with great sequencing comes great computation, and while cloud computing platforms bring high-performance computing directly into the hands of all who seek it, optimal design and configuration of a cloud compute cluster requires significant system administration expertise. We developed ViReflow, a user-friendly viral consensus sequence reconstruction pipeline enabling rapid analysis of viral sequence datasets leveraging Amazon Web Services (AWS) cloud compute resources and the Reflow system. ViReflow was developed specifically in response to the COVID-19 pandemic, but it is general to any viral pathogen. Importantly, when utilized with sufficient compute resources, ViReflow can trim, map, call variants, and call consensus sequences from amplicon sequence data from 1000 SARS-CoV-2 samples at 1000X depth in < 10 min, with no user intervention. ViReflow's simplicity, flexibility, and scalability make it an ideal tool for viral molecular epidemiological efforts.

Interspecies transcriptomics identify genes that underlie disproportionate foot growth in jerboas.

Despite the great diversity of vertebrate limb proportion and our deep understanding of the genetic mechanisms that drive skeletal elongation, little is known about how individual bones reach different lengths in any species. Here, we directly compare the transcriptomes of homologous growth cartilages of the mouse (Mus musculus) and bipedal jerboa (Jaculus jaculus), the latter of which has "mouse-like" arms but extremely long metatarsals of the feet. Intersecting gene-expression differences in metatarsals and forearms of the two species revealed that about 10% of orthologous genes are associated with the disproportionately rapid elongation of neonatal jerboa feet. These include genes and enriched pathways not previously associated with endochondral elongation as well as those that might diversify skeletal proportion in addition to their known requirements for bone growth throughout the skeleton. We also identified transcription regulators that might act as "nodes" for sweeping differences in genome expression between species. Among these, Shox2, which is necessary for proximal limb elongation, has gained expression in jerboa metatarsals where it has not been detected in other vertebrates. We show that Shox2 is sufficient to increase mouse distal limb length, and a nearby putative cis-regulatory region is preferentially accessible in jerboa metatarsals. In addition to mechanisms that might directly promote growth, we found evidence that jerboa foot elongation may occur in part by de-repressing latent growth potential. The genes and pathways that we identified here provide a framework to understand the modular genetic control of skeletal growth and the remarkable malleability of vertebrate limb proportion.

Chromatin Accessibility and Transcriptional Differences in Human Stem Cell-Derived Early-Stage Retinal Organoids.

Retinogenesis involves the specification of retinal cell types during early vertebrate development. While model organisms have been critical for determining the role of dynamic chromatin and cell-type specific transcriptional networks during this process, an enhanced understanding of the developing human retina has been more elusive due to the requirement for human fetal tissue. Pluripotent stem cell (PSC) derived retinal organoids offer an experimentally accessible solution for investigating the developing human retina. To investigate cellular and molecular changes in developing early retinal organoids, we developed SIX6-GFP and VSX2-tdTomato (or VSX2-h2b-mRuby3) dual fluorescent reporters. When differentiated as 3D organoids these expressed GFP at day 15 and tdTomato (or mRuby3) at day 25, respectively. This enabled us to explore transcriptional and chromatin related changes using RNA-seq and ATAC-seq from pluripotency through early retina specification. Pathway analysis of developing organoids revealed a stepwise loss of pluripotency, while optic vesicle and retina pathways became progressively more prevalent. Correlating gene transcription with chromatin accessibility in early eye field development showed that retinal cells underwent a clear change in chromatin landscape, as well as gene expression profiles. While each dataset alone provided valuable information, considering both in parallel provided an informative glimpse into the molecular nature eye development.

Wastewater sequencing uncovers early, cryptic SARS-CoV-2 variant transmission.

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing/sequencing capacity, which can also introduce biases. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here, we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We develop and deploy improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detect emerging variants of concern up to 14 days earlier in wastewater samples, and identify multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

The RNA structure alignment ontology.

Multiple sequence alignments are powerful tools for understanding the structures, functions, and evolutionary histories of linear biological macromolecules (DNA, RNA, and proteins), and for finding homologs in sequence databases. We address several ontological issues related to RNA sequence alignments that are informed by structure. Multiple sequence alignments are usually shown as two-dimensional (2D) matrices, with rows representing individual sequences, and columns identifying nucleotides from different sequences that correspond structurally, functionally, and/or evolutionarily. However, the requirement that sequences and structures correspond nucleotide-by-nucleotide is unrealistic and hinders representation of important biological relationships. High-throughput sequencing efforts are also rapidly making 2D alignments unmanageable because of vertical and horizontal expansion as more sequences are added. Solving the shortcomings of traditional RNA sequence alignments requires explicit annotation of the meaning of each relationship within the alignment. We introduce the notion of "correspondence," which is an equivalence relation between RNA elements in sets of sequences as the basis of an RNA alignment ontology. The purpose of this ontology is twofold: first, to enable the development of new representations of RNA data and of software tools that resolve the expansion problems with current RNA sequence alignments, and second, to facilitate the integration of sequence data with secondary and three-dimensional structural information, as well as other experimental information, to create simultaneously more accurate and more exploitable RNA alignments.

NoiseMaker: simulated screens for statistical assessment.

UNLABELLED: High-throughput screening (HTS) is a common technique for both drug discovery and basic research, but researchers often struggle with how best to derive hits from HTS data. While a wide range of hit identification techniques exist, little information is available about their sensitivity and specificity, especially in comparison to each other. To address this, we have developed the open-source NoiseMaker software tool for generation of realistically noisy virtual screens. By applying potential hit identification methods to NoiseMaker-simulated data and determining how many of the pre-defined true hits are recovered (as well as how many known non-hits are misidentified as hits), one can draw conclusions about the likely performance of these techniques on real data containing unknown true hits. Such simulations apply to a range of screens, such as those using small molecules, siRNAs, shRNAs, miRNA mimics or inhibitors, or gene over-expression; we demonstrate this utility by using it to explain apparently conflicting reports about the performance of the B score hit identification method. AVAILABILITY AND IMPLEMENTATION: NoiseMaker is written in C#, an ECMA and ISO standard language with compilers for multiple operating systems. Source code, a Windows installer and complete unit tests are available at http://sourceforge.net/projects/noisemaker. Full documentation and support are provided via an extensive help file and tool-tips, and the developers welcome user suggestions.