BACKGROUND: Nursing home residents have increased rates of intestinal colonisation with multidrug-resistant organisms (MDROs). We assessed the colonisation and spread of MDROs among this population, determined clinical risk factors for MDRO colonisation and investigated the role of the gut microbiota in providing colonisation resistance against MDROs. METHODS: We conducted a prospective cohort study in a Dutch nursing home. Demographical, epidemiological and clinical data were collected at four time points with 2-month intervals (October 2016-April 2017). To obtain longitudinal data, faecal samples from residents were collected for at least two time points. Ultimately, twenty-seven residents were included in the study and 93 faecal samples were analysed, of which 27 (29.0%) were MDRO-positive. Twelve residents (44.4%) were colonised with an MDRO at at least one time point throughout the 6-month study. RESULTS: Univariable generalised estimating equation logistic regression indicated that antibiotic use in the previous 2 months and hospital admittance in the previous year were associated with MDRO colonisation. Characterisation of MDRO isolates through whole-genome sequencing revealed Escherichia coli sequence type (ST)131 to be the most prevalent MDRO and ward-specific clusters of E. coli ST131 were identified. Microbiota analysis by 16S rRNA gene amplicon sequencing revealed no differences in alpha or beta diversity between MDRO-positive and negative samples, nor between residents who were ever or never colonised. Three bacterial taxa (Dorea, Atopobiaceae and Lachnospiraceae ND3007 group) were more abundant in residents never colonised with an MDRO throughout the 6-month study. An unexpectedly high abundance of Bifidobacterium was observed in several residents. Further investigation of a subset of samples with metagenomics showed that various Bifidobacterium species were highly abundant, of which B. longum strains remained identical within residents over time, but were different between residents. CONCLUSIONS: Our study provides new evidence for the role of the gut microbiota in colonisation resistance against MDROs in the elderly living in a nursing home setting. Dorea, Atopobiaceae and Lachnospiraceae ND3007 group may be associated with protection against MDRO colonisation. Furthermore, we report a uniquely high abundance of several Bifidobacterium species in multiple residents and excluded the possibility that this was due to probiotic supplementation.

BACKGROUND: It is well-established that cancer treatment substantially increases the risk of long-term adverse health outcomes among childhood cancer survivors. However, there is limited research on the underlying mechanisms. To elucidate the pathophysiology and a possible causal pathway from treatment exposures to cardiometabolic conditions, we conducted epigenome-wide association studies (EWAS) to identify the DNA methylation (DNAm) sites associated with cancer treatment exposures and examined whether treatment-associated DNAm sites mediate associations between specific treatments and cardiometabolic conditions. METHODS: We included 2052 survivors (median age 33.7 years) of European ancestry from the St. Jude Lifetime Cohort Study, a retrospective hospital-based study with prospective clinical follow-up. Cumulative doses of chemotherapy and region-specific radiation were abstracted from medical records. Seven cardiometabolic conditions were clinically assessed. DNAm profile was measured using MethylationEPIC BeadChip with blood-derived DNA. RESULTS: By performing multiple treatment-specific EWAS, we identified 935 5'-cytosine-phosphate-guanine-3' (CpG) sites mapped to 538 genes/regions associated with one or more cancer treatments at the epigenome-wide significance level (p < 9 × 10-8). Among the treatment-associated CpGs, 8 were associated with obesity, 63 with hypercholesterolemia, and 17 with hypertriglyceridemia (false discovery rate-adjusted p < 0.05). We observed substantial mediation by methylation at four independent CpGs (cg06963130, cg21922478, cg22976567, cg07403981) for the association between abdominal field radiotherapy (abdominal-RT) and risk of hypercholesterolemia (70.3%) and by methylation at three CpGs (cg19634849, cg13552692, cg09853238) for the association between abdominal-RT and hypertriglyceridemia (54.6%). In addition, three CpGs (cg26572901, cg12715065, cg21163477) partially mediated the association between brain-RT and obesity with a 32.9% mediation effect, and two CpGs mediated the association between corticosteroids and obesity (cg22351187, 14.2%) and between brain-RT and hypertriglyceridemia (cg13360224, 10.5%). Notably, several mediator CpGs reside in the proximity of well-established dyslipidemia genes: cg21922478 (ITGA1) and cg22976567 (LMNA). CONCLUSIONS: In childhood cancer survivors, cancer treatment exposures are associated with DNAm patterns present decades following the exposure. Treatment-associated DNAm sites may mediate the causal pathway from specific treatment exposures to certain cardiometabolic conditions, suggesting the utility of DNAm sites as risk predictors and potential mechanistic targets for future intervention studies.

BACKGROUND: DNA methylation (DNAm) is associated with gene regulation and estimated glomerular filtration rate (eGFR), a measure of kidney function. Decreased eGFR is more common among US Hispanics and African Americans. The causes for this are poorly understood. We aimed to identify trans-ethnic and ethnic-specific differentially methylated positions (DMPs) associated with eGFR using an agnostic, genome-wide approach. METHODS: The study included up to 5428 participants from multi-ethnic studies for discovery and 8109 participants for replication. We tested the associations between whole blood DNAm and eGFR using beta values from Illumina 450K or EPIC arrays. Ethnicity-stratified analyses were performed using linear mixed models adjusting for age, sex, smoking, and study-specific and technical variables. Summary results were meta-analyzed within and across ethnicities. Findings were assessed using integrative epigenomics methods and pathway analyses. RESULTS: We identified 93 DMPs associated with eGFR at an FDR of 0.05 and replicated 13 and 1 DMPs across independent samples in trans-ethnic and African American meta-analyses, respectively. The study also validated 6 previously published DMPs. Identified DMPs showed significant overlap enrichment with DNase I hypersensitive sites in kidney tissue, sites associated with the expression of proximal genes, and transcription factor motifs and pathways associated with kidney tissue and kidney development. CONCLUSIONS: We uncovered trans-ethnic and ethnic-specific DMPs associated with eGFR, including DMPs enriched in regulatory elements in kidney tissue and pathways related to kidney development. These findings shed light on epigenetic mechanisms associated with kidney function, bridging the gap between population-specific eGFR-associated DNAm and tissue-specific regulatory context.

BACKGROUND: Cancer researchers use cell lines, patient-derived xenografts, engineered mice, and tumoroids as models to investigate tumor biology and to identify therapies. The generalizability and power of a model derive from the fidelity with which it represents the tumor type under investigation; however, the extent to which this is true is often unclear. The preponderance of models and the ability to readily generate new ones has created a demand for tools that can measure the extent and ways in which cancer models resemble or diverge from native tumors. METHODS: We developed a machine learning-based computational tool, CancerCellNet, that measures the similarity of cancer models to 22 naturally occurring tumor types and 36 subtypes, in a platform and species agnostic manner. We applied this tool to 657 cancer cell lines, 415 patient-derived xenografts, 26 distinct genetically engineered mouse models, and 131 tumoroids. We validated CancerCellNet by application to independent data, and we tested several predictions with immunofluorescence. RESULTS: We have documented the cancer models with the greatest transcriptional fidelity to natural tumors, we have identified cancers underserved by adequate models, and we have found models with annotations that do not match their classification. By comparing models across modalities, we report that, on average, genetically engineered mice and tumoroids have higher transcriptional fidelity than patient-derived xenografts and cell lines in four out of five tumor types. However, several patient-derived xenografts and tumoroids have classification scores that are on par with native tumors, highlighting both their potential as faithful model classes and their heterogeneity. CONCLUSIONS: CancerCellNet enables the rapid assessment of transcriptional fidelity of tumor models. We have made CancerCellNet available as a freely downloadable R package ( https://github.com/pcahan1/cancerCellNet ) and as a web application ( http://www.cahanlab.org/resources/cancerCellNet_web ) that can be applied to new cancer models that allows for direct comparison to the cancer models evaluated here.

BACKGROUND: Genetic information is increasingly relevant across healthcare. Traditional genetic counseling (GC) may limit access to genetic information and may be more information and support than some individuals need. We report on the application and clinical implications of a framework to consistently integrate genetics expertise where it is most useful to patients. METHODS: The Clinical Genome Resource's (ClinGen) Consent and Disclosure Recommendations (CADRe) workgroup designed rubrics to guide pre- and post-genetic test communication. Using a standard set of testing indications, pre- and post-test rubrics were applied to 40 genetic conditions or testing modalities with diverse features, including variability in levels of penetrance, clinical actionability, and evidence supporting a gene-disease relationship. Final communication recommendations were reached by group consensus. RESULTS: Communication recommendations were determined for 478 unique condition-indication or testing-indication pairs. For half of the conditions and indications (238/478), targeted discussions (moderate communication depth) were the recommended starting communication level for pre- and post-test conversations. Traditional GC was recommended pre-test for adult-onset neurodegenerative conditions for individuals with no personal history and post-test for most conditions when genetic testing revealed a molecular diagnosis as these situations are likely higher in complexity and uncertainty. A brief communication approach was recommended for more straightforward conditions and indications (e.g., familial hypercholesterolemia; familial variant testing). CONCLUSIONS: The CADRe recommendations provide guidance for clinicians in determining the depth of pre- and post-test communication, strategically aligning the anticipated needs of patients with the starting communication approach. Shorter targeted discussions or brief communications are suggested for many tests and indications. Longer traditional GC consultations would be reserved for patients with more complex and uncertain situations where detailed information, education, and psychological support can be most beneficial. Future studies of the CADRe communication framework will be essential for determining if CADRe-informed care supports quality patient experience while improving access to genetic information across healthcare.

BACKGROUND: Identification of germline variation and somatic mutations is a major issue in human genetics. However, due to the limitations of DNA sequencing technologies and computational algorithms, our understanding of genetic variation and somatic mutations is far from complete. METHODS: In the present study, we performed whole-genome sequencing using long-read sequencing technology (Oxford Nanopore) for 11 Japanese liver cancers and matched normal samples which were previously sequenced for the International Cancer Genome Consortium (ICGC). We constructed an analysis pipeline for the long-read data and identified germline and somatic structural variations (SVs). RESULTS: In polymorphic germline SVs, our analysis identified 8004 insertions, 6389 deletions, 27 inversions, and 32 intra-chromosomal translocations. By comparing to the chimpanzee genome, we correctly inferred events that caused insertions and deletions and found that most insertions were caused by transposons and Alu is the most predominant source, while other types of insertions, such as tandem duplications and processed pseudogenes, are rare. We inferred mechanisms of deletion generations and found that most non-allelic homolog recombination (NAHR) events were caused by recombination errors in SINEs. Analysis of somatic mutations in liver cancers showed that long reads could detect larger numbers of SVs than a previous short-read study and that mechanisms of cancer SV generation were different from that of germline deletions. CONCLUSIONS: Our analysis provides a comprehensive catalog of polymorphic and somatic SVs, as well as their possible causes. Our software are available at https://github.com/afujimoto/CAMPHOR and https://github.com/afujimoto/CAMPHORsomatic .

Author information: (1)Department of Medical Genetics, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway. (2)Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 0310, Oslo, Norway. (3)Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318, Oslo, Norway. (4)Institute for Clinical Medicine, University of Oslo, Oslo, Norway. (5)Laboratory for Epigenetics and Environment, Centre National de Recherche en Génomique Humaine, CEA-Institut de Biologie François Jacob, University Paris-Saclay, Evry, France. (6)Department of Research, Vestre Viken Hospital Trust, Drammen, Norway. (7)Department of Medical Genetics, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway. xavier.tekpli@medisin.uio.no. (8)Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 0310, Oslo, Norway. xavier.tekpli@medisin.uio.no. (9)Department of Medical Genetics, Institute of Clinical Medicine, Faculty of Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway. v.n.kristensen@medisin.uio.no. (10)Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, 0310, Oslo, Norway. v.n.kristensen@medisin.uio.no. (11)Department of Clinical Molecular Biology and Laboratory Science (EpiGen), Division of Medicine, Akershus University Hospital, Lørenskog, Norway. v.n.kristensen@medisin.uio.no. (#)Contributed equally

BACKGROUND: Inflammatory breast cancer (IBC) has a highly invasive and metastatic phenotype. However, little is known about its genetic drivers. To address this, we report the largest cohort of whole-genome sequencing (WGS) of IBC cases. METHODS: We performed WGS of 20 IBC samples and paired normal blood DNA to identify genomic alterations. For comparison, we used 23 matched non-IBC samples from the Cancer Genome Atlas Program (TCGA). We also validated our findings using WGS data from the International Cancer Genome Consortium (ICGC) and the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We examined a wide selection of genomic features to search for differences between IBC and conventional breast cancer. These include (i) somatic and germline single-nucleotide variants (SNVs), in both coding and non-coding regions; (ii) the mutational signature and the clonal architecture derived from these SNVs; (iii) copy number and structural variants (CNVs and SVs); and (iv) non-human sequence in the tumors (i.e., exogenous sequences of bacterial origin). RESULTS: Overall, IBC has similar genomic characteristics to non-IBC, including specific alterations, overall mutational load and signature, and tumor heterogeneity. In particular, we observed similar mutation frequencies between IBC and non-IBC, for each gene and most cancer-related pathways. Moreover, we found no exogenous sequences of infectious agents specific to IBC samples. Even though we could not find any strongly statistically distinguishing genomic features between the two groups, we did find some suggestive differences in IBC: (i) The MAST2 gene was more frequently mutated (20% IBC vs. 0% non-IBC). (ii) The TGF β pathway was more frequently disrupted by germline SNVs (50% vs. 13%). (iii) Different copy number profiles were observed in several genomic regions harboring cancer genes. (iv) Complex SVs were more frequent. (v) The clonal architecture was simpler, suggesting more homogenous tumor-evolutionary lineages. CONCLUSIONS: Whole-genome sequencing of IBC manifests a similar genomic architecture to non-IBC. We found no unique genomic alterations shared in just IBCs; however, subtle genomic differences were observed including germline alterations in TGFβ pathway genes and somatic mutations in the MAST2 kinase that could represent potential therapeutic targets.

BACKGROUND: Genes with multiple co-active promoters appear common in brain, yet little is known about functional requirements for these potentially redundant genomic regulatory elements. SCN1A, which encodes the NaV1.1 sodium channel alpha subunit, is one such gene with two co-active promoters. Mutations in SCN1A are associated with epilepsy, including Dravet syndrome (DS). The majority of DS patients harbor coding mutations causing SCN1A haploinsufficiency; however, putative causal non-coding promoter mutations have been identified. METHODS: To determine the functional role of one of these potentially redundant Scn1a promoters, we focused on the non-coding Scn1a 1b regulatory region, previously described as a non-canonical alternative transcriptional start site. We generated a transgenic mouse line with deletion of the extended evolutionarily conserved 1b non-coding interval and characterized changes in gene and protein expression, and assessed seizure activity and alterations in behavior. RESULTS: Mice harboring a deletion of the 1b non-coding interval exhibited surprisingly severe reductions of Scn1a and NaV1.1 expression throughout the brain. This was accompanied by electroencephalographic and thermal-evoked seizures, and behavioral deficits. CONCLUSIONS: This work contributes to functional dissection of the regulatory wiring of a major epilepsy risk gene, SCN1A. We identified the 1b region as a critical disease-relevant regulatory element and provide evidence that non-canonical and seemingly redundant promoters can have essential function.

Most two-group statistical tests find broad patterns such as overall shifts in mean, median, or variance. These tests may not have enough power to detect effects in a small subset of samples, e.g., a drug that works well only on a few patients. We developed a novel statistical test targeting such effects relevant for clinical trials, biomarker discovery, feature selection, etc. We focused on finding meaningful associations in complex genetic diseases in gene expression, miRNA expression, and DNA methylation. Our test outperforms traditional statistical tests in simulated and experimental data and detects potentially disease-relevant genes with heterogeneous effects.

Author information: (1)New York Genome Center, New York, NY, USA. skasela@nygenome.org. (2)Department of Systems Biology, Columbia University, New York, NY, USA. skasela@nygenome.org. (3)Department of Internal Medicine, Section of Pulmonary, Critical Care, Allergy and Immunologic Diseases, Wake Forest School of Medicine, Winston-Salem, NC, USA. (4)New York Genome Center, New York, NY, USA. (5)Department of Systems Biology, Columbia University, New York, NY, USA. (6)Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA. (7)Division of Pulmonary, Critical Care, Allergy, & Sleep Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA. (8)Department of Medicine, Columbia University Medical Center, New York, NY, USA. (9)Division of Pulmonary and Critical Care Medicine, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA. (10)Division of Genetics, Genomics and Precision Medicine, Department of Medicine, University of Arizona, Tucson, AZ, USA. (11)Division of Pulmonary Medicine, Department of Medicine, National Jewish Health, Denver, CO, USA. (12)Division of Pulmonary and Critical Care, University of Iowa, Iowa City, IA, USA. (13)Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. (14)Department of Thoracic Medicine and Surgery, Lewis Katz School of Medicine at Temple University, Philadelphia, PA, USA. (15)Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Michigan Health System, Ann Arbor, MI, USA. (16)Medicine Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA. (17)Division of Pulmonary and Critical Care Medicine, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. (18)Division of Physiologic Imaging, Department of Radiology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA. (19)Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Weill Cornell Medicine, New York, NY, USA. (20)Department of Genetic Medicine, Weill Cornell Medicine, New York, NY, USA. (21)Division of Pulmonary, Critical Care, Sleep and Allergy, University of Illinois at Chicago, Chicago, IL, USA. (22)Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA. (23)Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA. (24)Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, University of Kansas School of Medicine, Kansas City, KS, USA. (25)Division of Allergy, Pulmonary, and Critical Care Medicine, Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA. (26)Department of Pathobiology, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA. (27)Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. (28)Department of Environmental and Occupational Health, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA. (29)Respiratory Institute, Cleveland Clinic, Cleveland, OH, USA. (30)Division of Infectious Diseases, University of California San Francisco, San Francisco, CA, USA. (31)Chan Zuckerberg Biohub, San Francisco, CA, USA. (32)New York Genome Center, New York, NY, USA. tlappalainen@nygenome.org. (33)Department of Systems Biology, Columbia University, New York, NY, USA. tlappalainen@nygenome.org. (34)Division of Pulmonary, Critical Care, Allergy, & Sleep Medicine, Department of Medicine, University of California San Francisco, San Francisco, CA, USA. stephanie.christenson@ucsf.edu. (#)Contributed equally

BACKGROUND: Preclinical studies and early clinical trials have shown that targeting cancer neoantigens is a promising approach towards the development of personalized cancer immunotherapies. DNA vaccines can be rapidly and efficiently manufactured and can integrate multiple neoantigens simultaneously. We therefore sought to optimize the design of polyepitope DNA vaccines and test optimized polyepitope neoantigen DNA vaccines in preclinical models and in clinical translation. METHODS: We developed and optimized a DNA vaccine platform to target multiple neoantigens. The polyepitope DNA vaccine platform was first optimized using model antigens in vitro and in vivo. We then identified neoantigens in preclinical breast cancer models through genome sequencing and in silico neoantigen prediction pipelines. Optimized polyepitope neoantigen DNA vaccines specific for the murine breast tumor E0771 and 4T1 were designed and their immunogenicity was tested in vivo. We also tested an optimized polyepitope neoantigen DNA vaccine in a patient with metastatic pancreatic neuroendocrine tumor. RESULTS: Our data support an optimized polyepitope neoantigen DNA vaccine design encoding long (≥20-mer) epitopes with a mutant form of ubiquitin (Ubmut) fused to the N-terminus for antigen processing and presentation. Optimized polyepitope neoantigen DNA vaccines were immunogenic and generated robust neoantigen-specific immune responses in mice. The magnitude of immune responses generated by optimized polyepitope neoantigen DNA vaccines was similar to that of synthetic long peptide vaccines specific for the same neoantigens. When combined with immune checkpoint blockade therapy, optimized polyepitope neoantigen DNA vaccines were capable of inducing antitumor immunity in preclinical models. Immune monitoring data suggest that optimized polyepitope neoantigen DNA vaccines are capable of inducing neoantigen-specific T cell responses in a patient with metastatic pancreatic neuroendocrine tumor. CONCLUSIONS: We have developed and optimized a novel polyepitope neoantigen DNA vaccine platform that can target multiple neoantigens and induce antitumor immune responses in preclinical models and neoantigen-specific responses in clinical translation.

Update of bioRxiv. 2020 Aug 05;:

Author information: (1)Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Ave NE S413A, Box 355065, Seattle, WA, 981095-5065, USA. (2)Baylor Genetics Laboratories, Houston, TX, USA. (3)Department of Molecular & Human Genetics, Baylor College of Medicine, Houston, TX, USA. (4)Center for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China. (5)Department of Anatomy, University of California, San Francisco, CA, USA. (6)Department of Psychiatry, University of California, San Francisco, CA, USA. (7)Weill Institute for Neurosciences, University of California at San Francisco, San Francisco, CA, USA. (8)The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA, USA. (9)Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands. (10)Department of Molecular Medicine and Surgery, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden. (11)Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden. (12)School of Medicine and the Robinson Research Institute, the University of Adelaide at the Women's and Children's Hospital, Adelaide, South Australia, Australia. (13)Genetics and Molecular Pathology, SA Pathology, Adelaide, South Australia, Australia. (14)South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia. (15)Laboratorio di Genetica Medica - ASST Papa Giovanni XXIII, Bergamo, Italy. (16)Department of Pediatrics, ASST Papa Giovanni XXIII, Bergamo, Italy. (17)Genetic Unit, Department of Pediatrics, Fondazione MBBM S. Gerardo Hospital, Monza, Italy. (18)Unit of Neuromuscular and Neurodegenerative Disorders, Department Neurosciences, Bambino Gesù Children's Hospital, IRCCS, 00146, Rome, Italy. (19)Institute of Human Genetics, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany. (20)Center on Human Development and Disability, University of Washington, Seattle, WA, USA. (21)Seattle Children's Autism Center, Seattle, WA, USA. (22)GeneDX, Gaithersburg, MD, USA. (23)Department of Pediatrics-Neurology, Baylor College of Medicine, Houston, TX, USA. (24)Texas Children's Hospital, Houston, TX, USA. (25)Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA. (26)The Atwal Clinic: Genomic & Personalized Medicine, Jacksonville, FL, USA. (27)Department of Pediatrics, Section of Genetics, University of Illinois at Chicago, Chicago, IL, USA. (28)Department of Pediatrics, University of California San Diego, San Diego, CA, USA. (29)Genetics/Dysmorphology, Rady Children's Hospital San Diego, San Diego, CA, USA. (30)Rady Children's Institute for Genomic Medicine, San Diego, CA, USA. (31)Department of Neurosciences, University of California San Diego, San Diego, CA, USA. (32)Department of Biomolecular Medicine, Ghent University Hospital, Ghent, Belgium. (33)Clinique de Génétique, Hôpital Jeanne de Flandre, Bâtiment Modulaire, CHU, 59037, Lille Cedex, France. (34)Sorbonne Universités, Centre de Référence déficiences intellectuelles de causes rares, département de génétique et embryologie médicale, Hôpital Trousseau, AP-HP, Paris, France. (35)Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA. (36)Department of Medical and Molecular Genetics, IU Health, Indianapolis, IN, USA. (37)Department of Genetics, Hadassah, Hebrew University Medical Center, Jerusalem, Israel. (38)Pediatric Neurology, Assuta-Ashdod University Hospital, Ashdod, Israel. (39)Health Sciences, Ben-Gurion University of the Negev, Beersheba, Israel. (40)Department of Clinical Genetics, Odense University Hospital, Odense, Denmark. (41)H C Andersen Chilldrens Hospital, Odense University Hospital, Odense, Denmark. (42)Gillette Children's Specialty Healthcare, Saint Paul, MN, USA. (43)Division of Clinical Genetics, Children's Mercy Kansas City, Kansas City, MO, USA. (44)The University of Missouri-Kansas City, School of Medicine, Kansas City, MO, USA. (45)Children's Mercy Kansas City, Center for Pediatric Genomic Medicine, Kansas City, MO, USA. (46)Oregon Health & Science University, Corvallis, OR, USA. (47)Department of Genetics, Hópital Pitié-Salpêtrière, Paris, France. (48)Department of Medical Genetics, University of Antwerp, Antwerp, Belgium. (49)Genetics Department, Cook Children's Hospital, Fort Worth, TX, USA. (50)Department of Medical Genetics, University of British Columbia, Vancouver, Canada. (51)BC Children's Hospital and BC Women's Hospital, Vancouver, BC, Canada. (52)Division of Developmental Pediatrics, Department of Pediatrics, BC Children's Hospital, University of British Columbia, Vancouver, BC, Canada. (53)Sunny Hill Health Centre for Children, Vancouver, BC, Canada. (54)Department of Pediatrics, The Children's Hospital at Montefiore, Bronx, NY, USA. (55)Institute of Human Genetics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. (56)Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA. (57)UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA. (58)Kaiser Permanente Southern California, Los Angeles, CA, USA. (59)The Permanente Medical Group, Oakland, CA, USA. (60)Northern Ireland Regional Genetics Service, Belfast City Hospital, Belfast, UK. (61)Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, WA, USA. (62)Department of Pediatrics, University of Washington, Seattle, WA, USA. (63)Brotman Baty Institute for Precision Medicine, Seattle, WA, USA. (64)Al Dupont Hospital for Children, Wilmington, DE, USA. (65)NYU Grossman School of Medicine, Department of Pediatrics, Clinical Genetic Services, New York, NY, USA. (66)Center for Human Genetics, KU Leuven and Leuven Autism Research (LAuRes), Leuven, Belgium. (67)Oasi Research Institute-IRCCS, Troina, Italy. (68)Department of Genetics, University Medical Center, Utrecht University, Utrecht, The Netherlands. (69)Rare Disease Institute, Children's National Health System, Washington, DC, USA. (70)Department of Genetics, Driscoll Children's Hospital, Corpus Christi, TX, USA. (71)Department of Pediatric Gastroenterology, Driscoll Children's Hospital, Corpus Christi, TX, USA. (72)Àrea de Genètica Clínica i Molecular, Hospital Vall d'Hebrón, Barcelona, Spain. (73)Centre de référence Anomalies du développement, CHU Grenoble-Alpes, Grenoble, France. (74)UF Innovation en Diagnostic Génomique des Maladies Rares, FHU-TRANSLAD, CHU Dijon Bourgogne and INSERM UMR1231 GAD, Université de Bourgogne Franche-Comté, F-21000, Dijon, France. (75)INSERM UMR 1231 Génétique des Anomalies du Développement, Université Bourgogne Franche-Comté, Dijon, France. (76)Centre de Référence Maladies Rares « déficience intellectuelle », Centre de Génétique, FHU-TRANSLAD, CHU Dijon Bourgogne, Dijon, France. (77)Centre de Référence Maladies Rares « Anomalies du Développement et Syndromes malformatifs »​ Université Bourgogne Franche-Comté, Dijon, France. (78)Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA. (79)Department of Genome Sciences, University of Washington School of Medicine, 3720 15th Ave NE S413A, Box 355065, Seattle, WA, 981095-5065, USA. eee@gs.washington.edu. (80)Howard Hughes Medical Institute, University of Washington, Seattle, WA, USA. eee@gs.washington.edu.

Update of medRxiv. 2020 Nov 04;:

BACKGROUND: Antimicrobial-resistant (AMR) Neisseria gonorrhoeae is an urgent threat to public health, as strains resistant to at least one of the two last-line antibiotics used in empiric therapy of gonorrhoea, ceftriaxone and azithromycin, have spread internationally. Whole genome sequencing (WGS) data can be used to identify new AMR clones and transmission networks and inform the development of point-of-care tests for antimicrobial susceptibility, novel antimicrobials and vaccines. Community-driven tools that provide an easy access to and analysis of genomic and epidemiological data is the way forward for public health surveillance. METHODS: Here we present a public health-focussed scheme for genomic epidemiology of N. gonorrhoeae at Pathogenwatch ( https://pathogen.watch/ngonorrhoeae ). An international advisory group of experts in epidemiology, public health, genetics and genomics of N. gonorrhoeae was convened to inform on the utility of current and future analytics in the platform. We implement backwards compatibility with MLST, NG-MAST and NG-STAR typing schemes as well as an exhaustive library of genetic AMR determinants linked to a genotypic prediction of resistance to eight antibiotics. A collection of over 12,000 N. gonorrhoeae genome sequences from public archives has been quality-checked, assembled and made public together with available metadata for contextualization. RESULTS: AMR prediction from genome data revealed specificity values over 99% for azithromycin, ciprofloxacin and ceftriaxone and sensitivity values around 99% for benzylpenicillin and tetracycline. A case study using the Pathogenwatch collection of N. gonorrhoeae public genomes showed the global expansion of an azithromycin-resistant lineage carrying a mosaic mtr over at least the last 10 years, emphasising the power of Pathogenwatch to explore and evaluate genomic epidemiology questions of public health concern. CONCLUSIONS: The N. gonorrhoeae scheme in Pathogenwatch provides customised bioinformatic pipelines guided by expert opinion that can be adapted to public health agencies and departments with little expertise in bioinformatics and lower-resourced settings with internet connection but limited computational infrastructure. The advisory group will assess and identify ongoing public health needs in the field of gonorrhoea, particularly regarding gonococcal AMR, in order to further enhance utility with modified or new analytic methods.

BACKGROUND: Currently, over half of breast cancer cases are unrelated to known risk factors, highlighting the importance of discovering other cancer-promoting factors. Since crosstalk between gut microbes and host immunity contributes to many diseases, we hypothesized that similar interactions could occur between the recently described breast microbiome and local immune responses to influence breast cancer pathogenesis. METHODS: Using 16S rRNA gene sequencing, we characterized the microbiome of human breast tissue in a total of 221 patients with breast cancer, 18 individuals predisposed to breast cancer, and 69 controls. We performed bioinformatic analyses using a DADA2-based pipeline and applied linear models with White's t or Kruskal-Wallis H-tests with Benjamini-Hochberg multiple testing correction to identify taxonomic groups associated with prognostic clinicopathologic features. We then used network analysis based on Spearman coefficients to correlate specific bacterial taxa with immunological data from NanoString gene expression and 65-plex cytokine assays. RESULTS: Multiple bacterial genera exhibited significant differences in relative abundance when stratifying by breast tissue type (tumor, tumor adjacent normal, high-risk, healthy control), cancer stage, grade, histologic subtype, receptor status, lymphovascular invasion, or node-positive status, even after adjusting for confounding variables. Microbiome-immune networks within the breast tended to be bacteria-centric, with sparse structure in tumors and more interconnected structure in benign tissues. Notably, Anaerococcus, Caulobacter, and Streptococcus, which were major bacterial hubs in benign tissue networks, were absent from cancer-associated tissue networks. In addition, Propionibacterium and Staphylococcus, which were depleted in tumors, showed negative associations with oncogenic immune features; Streptococcus and Propionibacterium also correlated positively with T-cell activation-related genes. CONCLUSIONS: This study, the largest to date comparing healthy versus cancer-associated breast microbiomes using fresh-frozen surgical specimens and immune correlates, provides insight into microbial profiles that correspond with prognostic clinicopathologic features in breast cancer. It additionally presents evidence for local microbial-immune interplay in breast cancer that merits further investigation and has preventative, diagnostic, and therapeutic potential.

Erratum in Genome Med. 2021 Oct 4;13(1):154.

Knowledge of the molecular etiology of neurodegenerative brain diseases (NBD) has substantially increased over the past three decades. Early genetic studies of NBD families identified rare and highly penetrant deleterious mutations in causal genes that segregate with disease. Large genome-wide association studies uncovered common genetic variants that influenced disease risk. Major developments in next-generation sequencing (NGS) technologies accelerated gene discoveries at an unprecedented rate and revealed novel pathways underlying NBD pathogenesis. NGS technology exposed large numbers of rare genetic variants of uncertain significance (VUS) in coding regions, highlighting the genetic complexity of NBD. Since experimental studies of these coding rare VUS are largely lacking, the potential contributions of VUS to NBD etiology remain unknown. In this review, we summarize novel findings in NBD genetic etiology driven by NGS and the impact of rare VUS on NBD etiology. We consider different mechanisms by which rare VUS can act and influence NBD pathophysiology and discuss why a better understanding of rare VUS is instrumental for deriving novel insights into the molecular complexity and heterogeneity of NBD. New knowledge might open avenues for effective personalized therapies.

BACKGROUND: ATPase family AAA-domain containing protein 3A (ATAD3A) is a nuclear-encoded mitochondrial membrane-anchored protein involved in diverse processes including mitochondrial dynamics, mitochondrial DNA organization, and cholesterol metabolism. Biallelic deletions (null), recessive missense variants (hypomorph), and heterozygous missense variants or duplications (antimorph) in ATAD3A lead to neurological syndromes in humans. METHODS: To expand the mutational spectrum of ATAD3A variants and to provide functional interpretation of missense alleles in trans to deletion alleles, we performed exome sequencing for identification of single nucleotide variants (SNVs) and copy number variants (CNVs) in ATAD3A in individuals with neurological and mitochondrial phenotypes. A Drosophila Atad3a Gal4 knockin-null allele was generated using CRISPR-Cas9 genome editing technology to aid the interpretation of variants. RESULTS: We report 13 individuals from 8 unrelated families with biallelic ATAD3A variants. The variants included four missense variants inherited in trans to loss-of-function alleles (p.(Leu77Val), p.(Phe50Leu), p.(Arg170Trp), p.(Gly236Val)), a homozygous missense variant p.(Arg327Pro), and a heterozygous non-frameshift indel p.(Lys568del). Affected individuals exhibited findings previously associated with ATAD3A pathogenic variation, including developmental delay, hypotonia, congenital cataracts, hypertrophic cardiomyopathy, and cerebellar atrophy. Drosophila studies indicated that Phe50Leu, Gly236Val, Arg327Pro, and Lys568del are severe loss-of-function alleles leading to early developmental lethality. Further, we showed that Phe50Leu, Gly236Val, and Arg327Pro cause neurogenesis defects. On the contrary, Leu77Val and Arg170Trp are partial loss-of-function alleles that cause progressive locomotion defects and whose expression leads to an increase in autophagy and mitophagy in adult muscles. CONCLUSION: Our findings expand the allelic spectrum of ATAD3A variants and exemplify the use of a functional assay in Drosophila to aid variant interpretation.

BACKGROUND: Mutations in the DMD gene encoding dystrophin-a critical structural element in muscle cells-cause Duchenne muscular dystrophy (DMD), which is the most common fatal genetic disease. Clustered regularly interspaced short palindromic repeat (CRISPR)-mediated gene editing is a promising strategy for permanently curing DMD. METHODS: In this study, we developed a novel strategy for reframing DMD mutations via CRISPR-mediated large-scale excision of exons 46-54. We compared this approach with other DMD rescue strategies by using DMD patient-derived primary muscle-derived stem cells (DMD-MDSCs). Furthermore, a patient-derived xenograft (PDX) DMD mouse model was established by transplanting DMD-MDSCs into immunodeficient mice. CRISPR gene editing components were intramuscularly delivered into the mouse model by adeno-associated virus vectors. RESULTS: Results demonstrated that the large-scale excision of mutant DMD exons showed high efficiency in restoring dystrophin protein expression. We also confirmed that CRISPR from Prevotella and Francisella 1(Cas12a)-mediated genome editing could correct DMD mutation with the same efficiency as CRISPR-associated protein 9 (Cas9). In addition, more than 10% human DMD muscle fibers expressed dystrophin in the PDX DMD mouse model after treated by the large-scale excision strategies. The restored dystrophin in vivo was functional as demonstrated by the expression of the dystrophin glycoprotein complex member β-dystroglycan. CONCLUSIONS: We demonstrated that the clinically relevant CRISPR/Cas9 could restore dystrophin in human muscle cells in vivo in the PDX DMD mouse model. This study demonstrated an approach for the application of gene therapy to other genetic diseases.