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ABSTRACT Clinical research studies are becoming increasingly complex resulting in compounded work burden and longer study cycle times, each fueling runaway costs. The impact of protocol complexity often results in inadequate recruitment and insufficient sample sizes, which challenges validity and generalizability. Understanding the need to provide an alternative model to engage researchers and sponsors and bringing clinical research opportunities to the broader community, clinical research networks (CRN) have been proposed and initiated in the United States and other parts of the world. We report on the Johns Hopkins Clinical Research Network (JHCRN), established in 2009 as a multi‐disease research collaboration between the academic medical centers and community hospitals/health systems. We have discussed vision, governance, infrastructure, participating hospitals' characteristics, and lessons learned in creating this partnership. Designed to leverage organized patient communities, community‐based investigators, and academic researchers, the JHCRN provides expedited research across nine health systems in the mid‐Atlantic region. With one IRB of record, a centralized contracting office, and a pool of dedicated network coordinators, it facilitates research partnerships to expand research collaborations among the differing sizes and types of hospitals/health systems in a region. As of August 2024, total 81 studies‐clinical trials, cohort studies, and comparative effectiveness research have been conducted, with funding from the NIH, private foundations, and industry. The JHCRN experience has enhanced understanding of the complexity of participating sites and associated ambulatory practices. In conclusion, the CRN, as an academic–community partnership, provides an infrastructure for multiple disease studies, shared risk, and increased investigator and volunteer engagement.

ABSTRACTThere has been an increase in the prevalence of Clostridioides difficile (C. diff) causing significant economic impact on the health care system. Although toxigenic C. diff carriage is recognized in infancy, there is limited data regarding its longitudinal trends, associated epidemiolocal risk factors and intestinal microbiome characteristics. The objectives of our longitudinal cohort study were to investigate temporal changes in the prevalence of toxigenic C.diff colonization in children up to 2 years, associated epidemiological and intestinal microbiome characteristics. Pregnant mothers were enrolled prenatally, and serial stool samples were collected from their children for 2 years. 2608 serial stool samples were collected from 817 children. 411/817 (50%) were males, and 738/817 (90%) were born full term. Toxigenic C.diff was detected in 7/569 (1%) of meconium samples, 116/624 (19%) of 2 m (month), 221/606 (37%) of 6 m, 227/574 (40%) of 12 m and 18/235 (8%) of 24 m samples. Infants receiving any breast milk at 6 m were less likely to be carriers at 2 m, 6 m and 12 m than those not receiving it. (p = 0.002 at 2 m, p

Abstract Background Preliminary data suggest that the urinary microbiome may play a role in bladder cancer. Information regarding the most suitable method of collecting urine specimens is needed for the large population studies needed to address this. To compare microbiome metrics resulting from 16S ribosomal RNA gene sequencing between midstream, voided specimens and those obtained at cystoscopy. Methods Adults, with a history of superficial urothelial cell carcinoma (non-muscle invasive bladder cancer) being followed with periodic surveillance cystoscopy had a urine sample collected by a mid-stream, voided technique and then from the bladder at cystoscopy. Urine samples underwent 16S ribosomal RNA gene sequencing on the Illumina MiSeq platform. Results 22 subjects (8 female, 14 male) were included. There was no significant difference in beta diversity (diversity between samples) in all samples between collection methods. However, analysis by sex revealed a difference between voided and cystoscopy samples from the same individual in males (p = 0.006, Adonis test) but not in females (p = 0.317, Adonis test). No differences were seen by collection method in any alpha diversity (diversity within a sample) measurement or differential abundance of taxa. Conclusions Beta diversity of the urine microbiome did differ by collection method for males only. This suggests that the urinary microbiomes of the two collection methods are not equivalent to each other, at least in males, which is the sex that bladder cancer occurs most frequently in. Therefore, the same collection method within a given study should be used.

The uncharacterized gene KIAA1109 has recently been associated with a congenital neurological malformation disorder that variably presents with arthrogryposis, craniofacial and/or cardiac abnormalities. We have identified two additional patients with compound heterozygous KIAA1109 variants presenting with the same neurological malformations. The mechanism whereby KIAA1109 loss of function causes this spectrum of disorders was the primary focus of our studies. We hypothesized that KIAA1109 function could be conserved relative to the fly gene tweek and examined endocytosis and endosome recycling in patient fibroblasts. Furthermore, we examined the structure of the cytoskeleton and cilia based on functional overlap with endocytosis and several known etiologies for neuronal migration disorders. Utilizing primary dermal fibroblasts from one patient and a healthy donor, we performed immunofluorescence and endocytosis assays to examine the endosomal, cytoskeletal, and ciliary cellular phenotypes. We found notable abnormalities in endosomal trafficking and endosome recycling pathways. We also observed changes in the actin cytoskeleton and cilia structural dynamics. We conclude that the function of KIAA1109 in humans may indeed overlap with the function of the Drosophila ortholog, resulting in perturbations to endosomal trafficking and the actin cytoskeleton. These alterations have ripple effects, altering many pathways that are critical for proper neuronal migration and embryonic development. Keywords: Cilia, Endocytosis, KIAA1109, Neurological malformation, Vesicular trafficking

Abstract Background Effective methods are needed to collect fecal samples from children for large-scale microbiota studies. Stool collected on fecal occult blood test (FOBT) cards that can be mailed provides an effective solution; however, the quality of sequencing resulting from this method is unknown. The aim of this study is to compare microbiota metrics of 16S ribosomal RNA (rRNA) gene sequencing from stool and meconium collected on FOBT cards with stool collected in an Eppendorf tube (ET) under different conditions. Methods Eight stool samples from children in diapers aged 0 month–2 years and three meconium samples were collected and stored as follows: (1) ≤ 2 days at room temperature (RT) in an ET, (2) 7 days at − 80 °C in an ET, (3) 3–5 days at RT on a FOBT card, (4) 7 days at RT on a FOBT card, and (5) 7 days at − 80 °C on a FOBT card. Samples stored at − 80 °C were frozen immediately. Each specimen/condition underwent 16S rRNA gene sequencing with replicates on the Illumina MiSeq. Alpha and beta diversity measures and relative abundance of major phyla were compared between storage conditions and container (ET vs. FOBT card), with pairwise comparison between different storage conditions and the “standard” of 7 days at − 80 °C in an ET and fresh stool in an ET. Results Stool samples clustered mainly by individual diaper (P

Background: There is a growing move to provide care for premature infants in a single family, private room neonatal intensive care unit (NICU) in place of the traditional shared space, open bay NICU. The resultant effect on the developing neonatal microbiota is unknown.Study Design: Stool and groin skin swabs were collected from infants in a shared-space NICU (old NICU) and a single-family room NICU (new NICU) on the same hospital campus. Metagenomic sequencing was performed and data analyzed by CosmosID bioinformatics software package.Results: There were no significant differences between the cohorts in gestational age, length of stay, and delivery mode; infants in the old NICU received significantly more antibiotics (p = 0.03). Differentially abundant antimicrobial resistance genes and virulence associated genes were found between the cohorts in stool and skin, with more differentially abundant antimicrobial resistance genes in the new NICU. The entire bacterial microbiota analyzed to the genus level significantly differed between cohorts in skin (p = 0.0001) but not in stool samples. There was no difference in alpha diversity between the two cohorts. DNA viruses and fungi were detected but did not differ between cohorts.Conclusion: Differences were seen in the resistome and virulome between the two cohorts with more differentially abundant antimicrobial resistance genes in the new NICU. This highlights the influence that different NICU environments can have on the neonatal microbiota. Whether the differences were due to the new NICU being a single-family NICU or located in a newly constructed building warrants exploration. Long term health outcomes from the differences observed must be followed longitudinally.

The study of germline mutations has been greatly enhanced by massive parallel sequencing technologies. Here the authors use deep sequencing data from nearly 700 parent-child trios to show maternal age has a small but significant correlation with the number of de novomutations in the offspring.

The meconium microbiome may provide insight into intrauterine and peripartum exposures and the very earliest intestinal pioneering microbes. Prenatal antibiotics have been associated with later obesity in children, which is thought to be driven by microbiome dependent mechanisms. However, there is little data regarding associations of prenatal or peripartum antibiotic exposure, with or without cesarean section (CS), with the features of the meconium microbiome. In this study, 16S ribosomal RNA gene sequencing was performed on bacterial DNA of meconium samples from 105 infants in a birth cohort study. After multivariable adjustment, delivery mode (p = 0.044), prenatal antibiotic use (p = 0.005) and peripartum antibiotic use (p < 0.001) were associated with beta diversity of the infant meconium microbiome. CS (vs. vaginal delivery) and peripartum antibiotics were also associated with greater alpha diversity of the meconium microbiome (Shannon and Simpson, p < 0.05). Meconium from infants born by CS (vs. vaginal delivery) had lower relative abundance of the genus Escherichia (p < 0.001). Prenatal antibiotic use and peripartum antibiotic use (both in the overall analytic sample and when restricting to vaginally delivered infants) were associated with differential abundance of several bacterial taxa in the meconium. Bacterial taxa in the meconium microbiome were also differentially associated with infant excess weight at 12 months of age, however, sample size was limited for this comparison. In conclusion, prenatal and peripartum antibiotic use along with CS delivery were associated with differences in the diversity and composition of the meconium microbiome. Whether or not these differences in the meconium microbiome portend risk for long-term health outcomes warrants further exploration.

The identification of DNA copy numbers from short-read sequencing data remains a challenge for both technical and algorithmic reasons. The raw data for these analyses are measured in tens to hundreds of gigabytes per genome; transmitting, storing and analyzing such large files is cumbersome, particularly for methods that analyze several samples simultaneously. We developed a very efficient representation of depth of coverage (150-1000x compression) that enables such analyses. Current methods for analyzing variants in whole-genome sequencing data frequently miss copy number variants (CNVs), particularly hemizygous deletions in the 1-100 kb range. To fill this gap, we developed a method to identify CNVs in individual genomes, based on comparison to joint profiles pre-computed from a large set of genomes.We analyzed depth of coverage in over 6000 high quality (>40x) genomes. The depth of coverage has strong sequence-specific fluctuations only partially explained by global parameters like %GC. To account for these fluctuations, we constructed multi-genome profiles representing the observed or inferred diploid depth of coverage at each position along the genome. These Reference Coverage Profiles (RCPs) take into account the diverse technologies and pipeline versions used. Normalization of the scaled coverage to the RCP followed by hidden Markov model (HMM) segmentation enables efficient detection of CNVs and large deletions in individual genomes.Use of pre-computed multi-genome coverage profiles improves our ability to analyze each individual genome. We make available RCPs and tools for performing these analyses on personal genomes. We expect the increased sensitivity and specificity for individual genome analysis to be critical for achieving clinical-grade genome interpretation.

Random Forest has become a standard data analysis tool in computational biology. However, extensions to existing implementations are often necessary to handle the complexity of biological datasets and their associated research questions. The growing size of these datasets requires high performance implementations. We describe CloudForest, a Random Forest package written in Go, which is particularly well suited for large, heterogeneous, genetic and biomedical datasets. CloudForest includes several extensions, such as dealing with unbalanced classes and missing values. Its flexible design enables users to easily implement additional extensions. CloudForest achieves fast running times by effective use of the CPU cache, optimizing for different classes of features and efficiently multi-threading. https://github.com/ilyalab/CloudForest.

Technological advances coupled with decreasing costs are bringing whole genome and whole exome sequencing closer to routine clinical use. One of the hurdles to clinical implementation is the high number of variants of unknown significance. For cancer-susceptibility genes, the difficulty in interpreting the clinical relevance of the genomic variants is compounded by the fact that most of what is known about these variants comes from the study of highly selected populations, such as cancer patients or individuals with a family history of cancer. The genetic variation in known cancer-susceptibility genes in the general population has not been well characterized to date. To address this gap, we profiled the nonsynonymous genomic variation in 158 genes causally implicated in carcinogenesis using high-quality whole genome sequences from an ancestrally diverse cohort of 681 healthy individuals. We found that all individuals carry multiple variants that may impact cancer susceptibility, with an average of 68 variants per individual. Of the 2,688 allelic variants identified within the cohort, most are very rare, with 75% found in only 1 or 2 individuals in our population. Allele frequencies vary between ancestral groups, and there are 21 variants for which the minor allele in one population is the major allele in another. Detailed analysis of a selected subset of 5 clinically important cancer genes, BRCA1, BRCA2, KRAS, TP53, and PTEN, highlights differences between germline variants and reported somatic mutations. The dataset can serve a resource of genetic variation in cancer-susceptibility genes in 6 ancestry groups, an important foundation for the interpretation of cancer risk from personal genome sequences.

BACKGROUND: Cancer stem cells (CSC) are thought to be responsible for tumor maintenance and heterogeneity. Bona fide CSC purified from tumor biopsies are limited in supply and this hampers study of CSC biology. Furthermore, purified stem-like CSC subpopulations from existing tumor lines are unstable in culture. Finding a means to overcome these technical challenges would be a useful goal. In a first effort towards this, we examined whether a chemical probe that promotes survival of murine embryonic stem cells without added exogenous factors can alter functional characteristics in extant tumor lines in a fashion consistent with a CSC phenotype. METHODOLOGY/PRINCIPAL FINDINGS: The seven tumor lines of the NCI60 colon subpanel were exposed to SC-1 (pluripotin), a dual kinase and GTPase inhibitor that promotes self-renewal, and then examined for tumorigenicity under limiting dilution conditions and clonogenic activity in soft agar. A statistically significant increase in tumor formation following SC-1 treatment was observed (p

Increased growth of residual tumors in the proximity of acute surgical wounds has been reported; however, the mechanisms of wound-promoted tumor growth remain unknown. Here, we used a syngeneic, orthotopic mouse model of breast cancer to study mechanisms of wound-promoted tumor growth. Our results demonstrate that exposure of metastatic mouse breast cancer cells (4T1) to SDF-1α, which is increased in wound fluid, results in increased tumor growth. Both, wounding and exposure of 4T1 cells to SDF-1α not only increased tumor growth, but also tumor cell proliferation rate and stromal collagen deposition. Conversely, systemic inhibition of SDF-1α signaling with the small molecule AMD 3100 abolished the effect of wounding, and decreased cell proliferation, collagen deposition, and neoangiogenesis to the levels observed in control animals. Furthermore, using different mouse strains we could demonstrate that the effect of wounding on tumor growth and SDF-1α levels is host dependent and varies between mouse strains. Our results show that wound-promoted tumor growth is mediated by elevated SDF-1α levels and indicate that the effect of acute wounds on tumor growth depends on the predetermined wound response of the host background and its predetermined wound response.

Carcinoma are complex societies of mutually interacting cells in which there is a progressive failure of normal homeostatic mechanisms, causing the parenchymal component to expand inappropriately and ultimately to disseminate to distant sites. When a cancer cell metastasizes, it first will be exposed to cancer associated fibroblasts in the immediate tumor microenvironment and then to normal fibroblasts as it traverses the underlying connective tissue towards the bloodstream. The interaction of tumor cells with stromal fibroblasts influences tumor biology by mechanisms that are not yet fully understood. Here, we report a role for normal stroma fibroblasts in the progression of invasive tumors to metastatic tumors. Using a coculture system of human metastatic breast cancer cells (MCF10CA1a) and normal murine dermal fibroblasts, we found that medium conditioned by cocultures of the two cell types (CoCM) increased migration and scattering of MCF10CA1a cells in vitro, whereas medium conditioned by homotypic cultures had little effect. Transient treatment of MCF10CA1a cells with CoCM in vitro accelerated tumor growth at orthotopic sites in vivo, and resulted in an expanded pattern of metastatic engraftment. The effects of CoCM on MCF10CA1a cells were dependent on small amounts of active TGF-beta1 secreted by fibroblasts under the influence of the tumor cells, and required intact ALK5-, p38-, and JNK signaling in the tumor cells. In conclusion, these results demonstrate that transient interactions between tumor cells and normal fibroblasts can modify the acellular component of the local microenvironment such that it induces long-lasting increases in tumorigenicity and alters the metastatic pattern of the cancer cells in vivo. TGF-beta appears to be a key player in this process, providing further rationale for the development of anti-cancer therapeutics that target the TGF-beta pathway.