SLC19A1 transports immunoreactive cyclic dinucleotides.

The accumulation of DNA in the cytosol serves as a key immunostimulatory signal associated with infections, cancer and genomic damage1,2. Cytosolic DNA triggers immune responses by activating the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway3. The binding of DNA to cGAS activates its enzymatic activity, leading to the synthesis of a second messenger, cyclic guanosine monophosphate-adenosine monophosphate (2'3'-cGAMP)4-7. This cyclic dinucleotide (CDN) activates STING8, which in turn activates the transcription factors interferon regulatory factor 3 (IRF3) and nuclear factor κ-light-chain-enhancer of activated B cells (NF-κB), promoting the transcription of genes encoding type I interferons and other cytokines and mediators that stimulate a broader immune response. Exogenous 2'3'-cGAMP produced by malignant cells9 and other CDNs, including those produced by bacteria10-12 and synthetic CDNs used in cancer immunotherapy13,14, must traverse the cell membrane to activate STING in target cells. How these charged CDNs pass through the lipid bilayer is unknown. Here we used a genome-wide CRISPR-interference screen to identify the reduced folate carrier SLC19A1, a folate-organic phosphate antiporter, as the major transporter of CDNs. Depleting SLC19A1 in human cells inhibits CDN uptake and functional responses, and overexpressing SLC19A1 increases both uptake and functional responses. In human cell lines and primary cells ex vivo, CDN uptake is inhibited by folates as well as two medications approved for treatment of inflammatory diseases, sulfasalazine and the antifolate methotrexate. The identification of SLC19A1 as the major transporter of CDNs into cells has implications for the immunotherapeutic treatment of cancer13, host responsiveness to CDN-producing pathogenic microorganisms11 and-potentially-for some inflammatory diseases

IDENTIFICATION AND FUNCTIONAL CHARACTERIZATION OF VPS41 AS A POTENTIAL GENETIC MODIFIER OF PENETRANCE IN P.G2019S LRRK2-ASSOCIATED PARKINSON’S DISEASE

peer reviewedTrait- or disease-associated genetic variants contribute to substantial variability and penetrance of complex traits and diseases in humans. While several causative genes and risk factors have been identified in familial forms of Parkinson’s disease (PD), it remains unclear how patient’s genomes shape the predisposition to develop PD. Mutations in LRRK2 are the most common autosomal dominantly inherited form of PD. The p.G2019S mutation displays incomplete penetrance and even within the group of affected individuals a broad range of age at disease onset (AAO) is observed. Through a whole genome sequencing approach in multiple families with p.G2019S LRRK2-associated PD, we have detected rare and common variants that might act as genetic modifiers of AAO. However, prioritizing disease-modifying variants and understanding their biological action remain a challenge. Using a new scoring system, we prioritized coding variants acting as modifiers of AAO in these families for functional in vitro validation. Among these modifiers we find GO term enrichment for genes with association to “neuron projection" but also Golgi apparatus associated vesicle and transport. From the candidates we selected the missense variant p.E432K in VPS41 (a member of the HOPS complex essential in lysosome/endosome trafficking), predicted by segregation analysis to confer protective effect on AAO in one of the families analysed. To this end, we assessed LRRK2 phenotypes in patient iPSC-derived dopaminergic neuron cultures. We found that VPS41 knockdown (KD) results in neurite outgrowth indistinguishable between p.G2019S and isogenic WT LRRK2 neurons. Lysosome and endosome morphology was altered upon VPS41 KD, but independent of LRRK2 status. The overexpression (OE) of WT and p.E432K VPS41 in HEK293T cells showed a differential starvation response, with increased localization of TFE3 to nuclei under baseline and starved conditions. In contrast, decreased TFE3 protein levels were previously reported in PD post-mortem substantia nigra compared to healthy controls. Through protein interaction assays we found that p.E432K VPS41 displays increased affinity to RAB7A indicating an increased interaction at the endosome/lysosome interface that is reported to be impaired in p.G2019S LRRK2 mutant neurons. Our findings lend support the concept of disease modifying variants incrementally contributing to the differential risk to develop PD in the context of LRRK2 mutations. We are currently further investigating how p.E432K VPS41 affects neurite outgrowth, endosome/lysosome morphology and autophagy in patient derived neurons and their controls.R-AGR-0592 - FNR - NCER-PD Phase II Coordination (01/06/2015 - 30/11/2023) - KRÜGER Rejk

Automated and Accurate Estimation of Gene Family Abundance from Shotgun Metagenomes

Shotgun metagenomic DNA sequencing is a widely applicable tool for characterizing the functions that are encoded by microbial communities. Several bioinformatic tools can be used to functionally annotate metagenomes, allowing researchers to draw inferences about the functional potential of the community and to identify putative functional biomarkers. However, little is known about how decisions made during annotation affect the reliability of the results. Here, we use statistical simulations to rigorously assess how to optimize annotation accuracy and speed, given parameters of the input data like read length and library size. We identify best practices in metagenome annotation and use them to guide the development of the Shotgun Metagenome Annotation Pipeline (ShotMAP). ShotMAP is an analytically flexible, end-to-end annotation pipeline that can be implemented either on a local computer or a cloud compute cluster. We use ShotMAP to assess how different annotation databases impact the interpretation of how marine metagenome and metatranscriptome functional capacity changes across seasons. We also apply ShotMAP to data obtained from a clinical microbiome investigation of inflammatory bowel disease. This analysis finds that gut microbiota collected from Crohn’s disease patients are functionally distinct from gut microbiota collected from either ulcerative colitis patients or healthy controls, with differential abundance of metabolic pathways related to host-microbiome interactions that may serve as putative biomarkers of diseaseData Availability Statement: The Gilbert et al. L4 metagenomes and metatranscriptomes are available from the MG-RAST database (project number 109,  http://metagenomics.anl.gov/metagenomics.cgi?page=MetagenomeProject&project=109), the Qin et al. MetaHIT inflammatory bowel disease metagenomes are available in the EBI (accession ERA000116), and the Nielsen et al. MGS inflammatory bowel disease metagenomes are available in the EBI (accession ERP002061)

Methods for Obtaining and Analyzing Whole Chloroplast Genome Sequences

During the past decade there has been a rapid increase in our understanding of plastid genome organization and evolution due to the availability of many new completely sequenced genomes. Currently there are 43 complete genomes published and ongoing projects are likely to increase this sampling to nearly 200 genomes during the next five years. Several groups of researchers including ours have been developing new techniques for gathering and analyzing entire plastid genome sequences and details of these developments are summarized in this chapter. The most important recent developments that enhance our ability to generate whole chloroplast genome sequences involve the generation of pure fractions of chloroplast genomes by whole genome amplification using rolling circular amplification, cloning genomes into Fosmid or BAC vectors, and the development of an organellar annotation program (DOGMA). In addition to providing details of these methods, we provide an overview of methods for analyzing complete plastid genome sequences for repeats and gene content, as well as approaches for using gene order and sequence data for phylogeny reconstruction. This explosive increase in the number of sequenced plastid genomes and improved computational tools will provide many insights into the evolution of these genomes and much new data for assessing relationships at deep nodes in plants and other photosynthetic organisms

Repertoire of microRNAs in Epithelial Ovarian Cancer as Determined by Next Generation Sequencing of Small RNA cDNA Libraries

MicroRNAs (miRNAs) are small regulatory RNAs that are implicated in cancer pathogenesis and have recently shown promise as blood-based biomarkers for cancer detection. Epithelial ovarian cancer is a deadly disease for which improved outcomes could be achieved by successful early detection and enhanced understanding of molecular pathogenesis that leads to improved therapies. A critical step toward these goals is to establish a comprehensive view of miRNAs expressed in epithelial ovarian cancer tissues as well as in normal ovarian surface epithelial cells.We used massively parallel pyrosequencing (i.e., "454 sequencing") to discover and characterize novel and known miRNAs expressed in primary cultures of normal human ovarian surface epithelium (HOSE) and in tissue from three of the most common histotypes of ovarian cancer. Deep sequencing of small RNA cDNA libraries derived from normal HOSE and ovarian cancer samples yielded a total of 738,710 high-quality sequence reads, generating comprehensive digital profiles of miRNA expression. Expression profiles for 498 previously annotated miRNAs were delineated and we discovered six novel miRNAs and 39 candidate miRNAs. A set of 124 miRNAs was differentially expressed in normal versus cancer samples and 38 miRNAs were differentially expressed across histologic subtypes of ovarian cancer. Taqman qRT-PCR performed on a subset of miRNAs confirmed results of the sequencing-based study.This report expands the body of miRNAs known to be expressed in epithelial ovarian cancer and provides a useful resource for future studies of the role of miRNAs in the pathogenesis and early detection of ovarian cancer

Genome-wide Analyses Identify KIF5A as a Novel ALS Gene

To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.Peer reviewe

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Algorithms for the analysis of whole genomes

textWith the advent of whole genome sequencing, we now have an abundance of whole genomes which have been sequenced and we have entered an era when algorithms can address problems at the whole genome level. In the past, sequencing efforts often focused on a single gene, and therefore, existing algorithms are at the scale of a single gene. With whole genome sequencing, we have access to sequence data for the entire genome of an organism or an organelle and algorithms are needed for whole genome analysis. In this research, we have addressed new computational problems that have arisen out of the availability and abundance of whole genome data. In genome annotation, all of the genes of a genome are located and identified in preparation for publication of the complete genome sequence. We address the problem of genome annotation with a software package that allows researchers to locate and identify all the genes in a genome and prepare the genome for direct submission to GenBank. A difficult problem that arises in the annotation of organellar genomes is the identification of animal mitochondrial transfer RNA genes. We present an experimental evaluation a set of methods (including our own) for identifying tRNAs. The problem of reconstructing phylogenies from gene order data involves recreating the evolutionary history of a set of organisms based on the order and direction of the genes in the genomes. This can give insight into mechanisms of large-scale evolutionary events. We present a new method for gene order phylogeny reconstruction, as well as improvements to an existing method, and evaluate the results on both real and simulated datasets. Finally, we address the problem of identification of regulatory elements. Understanding gene expression is one of the most pressing unsolved problems in molecular biology today because gene expression controls all of the metabolic and developmental processes in an organism. We present a new method which uses a comparative genomics approach which is made possible now that we have access to the complete DNA sequences of many sets of related organisms.Computer Science

Understanding shared variation in SARS-CoV-2 genomes

The project is a collaborative effort of investigators from the University of California, Berkeley's Innovative Genomics Institute (IGI) and School of Public Health (SPH); Kaiser Permanente Northern California (KPNC); and the California Department of Public Health (CDPH), with administrative and programmatic support provided by Heluna Health. Over the project period, the collaborating investigators will analyze approximately 35,000 genomes of SARS-CoV-2 specimens obtained from KPNC members and sequenced by the CDPH through its COVIDNet activities. By combining results from the genomic analysis of low-frequency alleles with clinical and epidemiologic data available in patient records, including demographic variables, COVID-19 vaccination status (dates of vaccination; number of doses; manufacturer), COVID-19 disease severity, and underlying medical conditions, we assessed which shared genomic variations are associated with a greater risk of symptomatic infection and severe clinical outcomes; COVID-19 vaccine effectiveness; and transmission of SARS-CoV-2 in the household. The project and its results can serve as a model for community-based monitoring of the evolution and spread of SARS-CoV-2 and use of the data to inform decisions about the formulation and use of COVID-19 vaccines, including booster doses and next-generation vaccines.These are fasta files and tab-delimited files and can be opened with any editor (fasta) or excel (mutation files).Funding provided by: Rockefeller FoundationCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000877Award Number: 0889.0101Sample collection Our samples are from Kaiser Northern California patients testing positive for SARS-CoV-2 starting June 1, 2021, and through the present. The RNA is sent to the California Department of Public Health (CDPH) lab to be sequenced by COVIDNet–a consortium of primarily UC system labs helping CDPH with the overflow and backlog of samples. Once the genomes have been sequenced, the lineage information and unique deidentified PAUI number are returned to Kaiser where this information is recorded. Metadata from this list of PAUI's is sent weekly to UC Berkeley. The KPNC sequencing data is returned to us through a third party that is processing all CDPH genomes and stored on a server at UC Berkeley and matched with metadata using PAUI's. Sequence analysis The raw sequencing data is processed through a SARS-CoV-2 analysis pipeline that has been modified for this work as follows. Adapter removal and trimming are performed using bbduk. The reads are then aligned to the Wuhan reference genome using minimap2 followed by primer trimming using iVAR . We next create a pileup file using samtools and use that input to create a consensus file. This consensus file is created with iVAR using a minimum depth of 10 reads and majority rule for base calling. We next use iVAR to call variants from the pileup file where we set the threshold for calling a mutation to be 0.01. This will call mutations for any loci where at least one percent of the reads are non-reference. This very low threshold allows us to capture all variation that is seen in the sequencing data. The list of variants is then annotated with the gene and amino acid change (if there is one), and whether the mutation is considered defining in any SARS-CoV-2 variants and whether that mutation is seen in only one variant. This dataset includes the fasta consensus sequences and mutation calls for each genome