Machine learning classifier approaches for predicting response to RTK-type-III inhibitors demonstrate high accuracy using transcriptomic signatures and ex vivo data.

Motivation: The application of machine learning (ML) techniques in the medical field has demonstrated both successes and challenges in the precision medicine era. The ability to accurately classify a subject as a potential responder versus a nonresponder to a given therapy is still an active area of research pushing the field to create new approaches for applying machine-learning techniques. In this study, we leveraged publicly available data through the BeatAML initiative. Specifically, we used gene count data, generated via RNA-seq, from 451 individuals matched with ex vivo data generated from treatment with RTK-type-III inhibitors. Three feature selection techniques were tested, principal component analysis, Shapley Additive Explanation (SHAP) technique and differential gene expression analysis, with three different classifiers, XGBoost, LightGBM and random forest (RF). Sensitivity versus specificity was analyzed using the area under the curve (AUC)-receiver operating curves (ROCs) for every model developed. Results: Our work demonstrated that feature selection technique, rather than the classifier, had the greatest impact on model performance. The SHAP technique outperformed the other feature selection techniques and was able to with high accuracy predict outcome response, with the highest performing model: Foretinib with 89% AUC using the SHAP technique and RF classifier. Our ML pipelines demonstrate that at the time of diagnosis, a transcriptomics signature exists that can potentially predict response to treatment, demonstrating the potential of using ML applications in precision medicine efforts. Availability and implementation: https://github.com/UD-CRPL/RCDML. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

KMT2A-D pathogenicity, prevalence, and variation according to a population database.

INTRODUCTION: The KMT2 family of genes is essential epigenetic regulators promoting gene expression. The gene family contains three subgroups, each with two paralogues: KMT2A and KMT2B; KMT2C and KMT2D; KMT2F and KMT2G. KMT2A-D are among the most frequent somatically altered genes in several different cancer types. Somatic KMT2A rearrangements are well-characterized in infant leukemia (IL), and growing evidence supports the role of additional family members (KMT2B, KMT2C, and KMT2D) in leukemogenesis. Enrichment of rare heterozygous frameshift variants in KMT2A and C has been reported in acute myeloid leukemia (AML), IL, and solid tumors. Currently, the non-synonymous variation, prevalence, and penetrance of these four genes are unknown. METHODS: This study determined the prevalence of pathogenic/likely pathogenic (P/LP) germline KMT2A-D variants in a cancer-free adult population from the Genome Aggregation Database (gnomAD). Two methods of variant interpretation were utilized: a manual genomic variant interpretation and an automated ACMG pipeline. RESULTS: The ACMG pipeline identified considerably fewer P/LP variants (n = 89) compared to the manual method (n = 660) in all 4 genes. Consequently, the total P/LP prevalence and allele frequency (AF) were higher in the manual method (1:112, AF = 4.46E-03) than in ACMG (1:832, AF = 6.01E-04). Multiple ancestry-exclusive P/LP variants were identified along with an increased frequency in males compared to females. Many of these variants identified in this population database are also associated with severe juvenile conditions. CONCLUSION: These data demonstrate that putatively functional germline variation in these developmentally important genes is more common than previously appreciated and identification in cancer-free adults may indicate incomplete penetrance for many of these variants. Future research should examine a genetic predisposing role in IL and other pediatric cancers.

DNA Methylation Analysis Reveals Distinct Patterns in Satellite Cell-Derived Myogenic Progenitor Cells of Subjects with Spastic Cerebral Palsy.

Spastic type cerebral palsy (CP) is a complex neuromuscular disorder that involves altered skeletal muscle microanatomy and growth, but little is known about the mechanisms contributing to muscle pathophysiology and dysfunction. Traditional genomic approaches have provided limited insight regarding disease onset and severity, but recent epigenomic studies indicate that DNA methylation patterns can be altered in CP. Here, we examined whether a diagnosis of spastic CP is associated with intrinsic DNA methylation differences in myoblasts and myotubes derived from muscle resident stem cell populations (satellite cells; SCs). Twelve subjects were enrolled (6 CP; 6 control) with informed consent/assent. Skeletal muscle biopsies were obtained during orthopedic surgeries, and SCs were isolated and cultured to establish patient-specific myoblast cell lines capable of proliferation and differentiation in culture. DNA methylation analyses indicated significant differences at 525 individual CpG sites in proliferating SC-derived myoblasts (MB) and 1774 CpG sites in differentiating SC-derived myotubes (MT). Of these, 79 CpG sites were common in both culture types. The distribution of differentially methylated 1 Mbp chromosomal segments indicated distinct regional hypo- and hyper-methylation patterns, and significant enrichment of differentially methylated sites on chromosomes 12, 13, 14, 15, 18, and 20. Average methylation load across 2000 bp regions flanking transcriptional start sites was significantly different in 3 genes in MBs, and 10 genes in MTs. SC derived MBs isolated from study participants with spastic CP exhibited fundamental differences in DNA methylation compared to controls at multiple levels of organization that may reveal new targets for studies of mechanisms contributing to muscle dysregulation in spastic CP.

Integrated stem cell signature and cytomolecular risk determination in pediatric acute myeloid leukemia.

Relapsed or refractory pediatric acute myeloid leukemia (AML) is associated with poor outcomes and relapse risk prediction approaches have not changed significantly in decades. To build a robust transcriptional risk prediction model for pediatric AML, we perform RNA-sequencing on 1503 primary diagnostic samples. While a 17 gene leukemia stem cell signature (LSC17) is predictive in our aggregated pediatric study population, LSC17 is no longer predictive within established cytogenetic and molecular (cytomolecular) risk groups. Therefore, we identify distinct LSC signatures on the basis of AML cytomolecular subtypes (LSC47) that were more predictive than LSC17. Based on these findings, we build a robust relapse prediction model within a training cohort and then validate it within independent cohorts. Here, we show that LSC47 increases the predictive power of conventional risk stratification and that applying biomarkers in a manner that is informed by cytomolecular profiling outperforms a uniform biomarker approach.

CD24 Expression Dampens the Basal Antiviral State in Human Neuroblastoma Cells and Enhances Permissivity to Zika Virus Infection.

Zika virus (ZIKV) exhibits distinct selectivity for infection of various cells and tissues, but how host cellular factors modulate varying permissivity remains largely unknown. Previous studies showed that the neuroblastoma cell line SK-N-AS (expressing low levels of cellular protein CD24) was highly restricted for ZIKV infection, and that this restriction was relieved by ectopic expression of CD24. We tested the hypothesis that CD24 expression allowed ZIKV replication by suppression of the antiviral response. SK-N-AS cells expressing an empty vector (termed CD24-low cells) showed elevated basal levels of phosphorylated STAT1, IRF-1, IKKE, and NFκB. In response to exogenously added type I interferon (IFN-I), CD24-low cells had higher-level induction of antiviral genes and activity against two IFN-I-sensitive viruses (VSV and PIV5-P/V) compared to SK-N-AS cells with ectopic CD24 expression (termed CD24-high cells). Media-transfer experiments showed that the inherent antiviral state of CD24-low cells was not dependent on a secreted factor such as IFN-I. Transcriptomics analysis revealed that CD24 expression decreased expression of genes involved in intracellular antiviral pathways, including IFN-I, NFκB, and Ras. Our findings that CD24 expression in neuroblastoma cells represses intracellular antiviral pathways support the proposal that CD24 may represent a novel biomarker in cancer cells for susceptibility to oncolytic viruses.

A research agenda to support the development and implementation of genomics-based clinical informatics tools and resources.

OBJECTIVE: The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings. MATERIALS AND METHODS: Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy. RESULTS: Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address. DISCUSSION: Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.

Impact of Early Feeding: Metagenomics Analysis of the Infant Gut Microbiome.

Background: Different feeding regimens in infancy alter the gastrointestinal (gut) microbial environment. The fecal microbiota in turn influences gastrointestinal homeostasis including metabolism, immune function, and extra-/intra-intestinal signaling. Advances in next generation sequencing (NGS) have enhanced our ability to study the gut microbiome of breast-fed (BF) and formula-fed (FF) infants with a data-driven hypothesis approach. Methods: Next generation sequencing libraries were constructed from fecal samples of BF (n=24) and FF (n=10) infants and sequenced on an Illumina HiSeq 2500. Taxonomic classification of the NGS data was performed using the Sunbeam/Kraken pipeline and a functional analysis at the gene level was performed using publicly available algorithms, including BLAST, and custom scripts. Differentially represented genera, genes, and NCBI Clusters of Orthologous Genes (COG) were determined between cohorts using count data and R (statistical packages edgeR and DESeq2). Results: Thirty-nine genera were found to be differentially represented between the BF and FF cohorts (FDR ≤ 0.01) including Parabacteroides, Enterococcus, Haemophilus, Gardnerella, and Staphylococcus. A Welch t-test of the Shannon diversity index for BF and FF samples approached significance (p=0.061). Bray-Curtis and Jaccard distance analyses demonstrated clustering and overlap in each analysis. Sixty COGs were significantly overrepresented and those most significantly represented in BF vs. FF samples showed dichotomy of categories representing gene functions. Over 1,700 genes were found to be differentially represented (abundance) between the BF and FF cohorts. Conclusions: Fecal samples analyzed from BF and FF infants demonstrated differences in microbiota genera. The BF cohort includes greater presence of beneficial genus Bifidobacterium. Several genes were identified as present at different abundances between cohorts indicating differences in functional pathways such as cellular defense mechanisms and carbohydrate metabolism influenced by feeding. Confirmation of gene level NGS data via PCR and electrophoresis analysis revealed distinct differences in gene abundances associated with important biologic pathways.

Genomic surveillance of SARS-CoV-2 in the state of Delaware reveals tremendous genomic diversity.

The use of next generation sequencing is critical for the surveillance of severe acute respiratory syndrome coronavirus 2, SARS-CoV-2, transmission, as single base mutations have been identified with differences in infectivity. A total of 1,459 high quality samples were collected, sequenced, and analyzed in the state of Delaware, a location that offers a unique perspective on transmission given its proximity to large international airports on the east coast. Pangolin and Nextclade were used to classify these sequences into 16 unique clades and 88 lineages. A total of 411 samples belonging to the Alpha 20I/501Y.V1 (B.1.1.7) strain of concern were identified, as well as one sample belonging to Beta 20H/501.V2 (B.1.351), thirteen belonging to Epsilon 20C/S:452R (B.1.427/B.1.429), two belonging to Delta 20A/S:478K (B.1.617.2), and 15 belonging to Gamma 20J/501Y.V3 (p.1). A total of 2217 unique coding mutations were observed with an average of 17.7 coding mutations per genome. These data paired with continued sample collection and sequencing will give a deeper understanding of the spread of SARS-CoV-2 strains within Delaware and its surrounding areas.

The PNPLA3 rs738409 Variant but not MBOAT7 rs641738 is a Risk Factor for Nonalcoholic Fatty Liver Disease in Obese U.S. Children of Hispanic Ethnicity.

PURPOSE: The rs641738 C>T in membrane-bound O-acyltransferase domain-containing protein 7 (MBOAT7) is implicated, along with the rs738409 C>G polymorphism in patatin-like phospholipase domain-containing protein 3 (PNPLA3), in nonalcoholic fatty liver disease (NAFLD). The association of these polymorphisms and NAFLD are investigated in Hispanic children with obesity. METHODS: Obese children with and without NAFLD were enrolled at a pediatric tertiary care health system and genotyped for MBOAT7 rs641738 C>T and PNPLA3 rs738409 C>G. NAFLD was characterized by the ultrasonographic presence of hepatic steatosis along with persistently elevated liver enzymes. Genetic variants and demographic and biochemical data were analyzed for the effects on NAFLD. RESULTS: Among 126 enrolled subjects, 84 in the case group had NAFLD and 42 in the control group did not. The two groups had similar demographic distribution. NAFLD was associated with abnormal liver enzymes and elevated triglycerides and cholesterol (p<0.05). Children with NAFLD had higher percentage of PNPLA3 GG genotype at 70.2% versus 31.0% in non-NAFLD, and lower MBOAT7 TT genotype at 4.8% versus 16.7% in non-NAFLD (p<0.05). PNPLA3 rs738409 C>G had an additive effect in NAFLD; however, MBOAT7 rs641738 C>T had no effects alone or synergistically with PNPLA3 polymorphism. NAFLD risk increased 3.7-fold in subjects carrying PNPLA3 GG genotype and decreased in MBOAT7 TT genotype. CONCLUSION: In Hispanic children with obesity, PNPLA3 rs738409 C>G polymorphism increased the risk for NAFLD. The role of MBOAT7 rs641738 variant in NAFLD is less evident.

Transcriptional analysis of muscle tissue and isolated satellite cells in spastic cerebral palsy.

AIM: To analyze transcriptomes from muscle tissue and cells to improve our understanding of differences in gene expression and molecular function in cerebral palsy (CP) muscle. METHOD: In this case-control study, eight participants with CP (five males, three females; mean [SD] age 14y 2mo [1y 8mo]) and 11 comparison individuals (eight males, three females; mean [SD] age 14y 0mo [2y 6mo]) were enrolled after informed consent/assent and skeletal muscle was obtained during surgery. RNA was extracted from tissue and from primary satellite cells grown to form myotubes in vitro. RNA sequencing data were analyzed using validated informatics pipelines. RESULTS: Analysis identified expression of 6308 genes in the tissue samples and 7459 in the cultured cells. Significant differential expression between CP and control was identified in 87 genes in the tissue and 90 genes in isolated satellite cell-derived myotube cultures. INTERPRETATION: Both tissue and cell analyses identified differential expression of genes associated with muscle development and multiple pathways of interest. What this paper adds Expression differences were found in muscle tissue and in isolated muscle cells. There was low variability in expression among cells isolated from different muscles. Expression differences suggest complex functional alterations in spastic cerebral palsy.

Implementation of a learning healthcare system for sickle cell disease.

OBJECTIVE: Using sickle cell disease (SCD) as a model, the objective of this study was to create a comprehensive learning healthcare system to support disease management and research. A multidisciplinary team developed a SCD clinical data dictionary to standardize bedside data entry and inform a scalable environment capable of converting complex electronic healthcare records (EHRs) into knowledge accessible in real time. MATERIALS AND METHODS: Clinicians expert in SCD care developed a data dictionary to describe important SCD-associated health maintenance and adverse events. The SCD data dictionary was deployed in the EHR using EPIC SmartForms, an efficient bedside data entry tool. Additional data elements were extracted from the EHR database (Clarity) using Pentaho Data Integration and stored in a data analytics database (SQL). A custom application, the Sickle Cell Knowledgebase, was developed to improve data analysis and visualization. Utilization, accuracy, and completeness of data entry were assessed. RESULTS: The SCD Knowledgebase facilitates generation of patient-level and aggregate data visualization, driving the translation of data into knowledge that can impact care. A single patient can be selected to monitor health maintenance, comorbidities, adverse event frequency and severity, and medication dosing/adherence. CONCLUSIONS: Disease-specific data dictionaries used at the bedside will ultimately increase the meaningful use of EHR datasets to drive consistent clinical data entry, improve data accuracy, and support analytics that will facilitate quality improvement and research.

Germline Sequencing Identifies Rare Variants in Finnish Subjects with Familial Germ Cell Tumors.

PURPOSE: Pediatric germ cell tumors are rare, representing about 3% of childhood malignancies in children less than 15 years of age, presenting in neonates or adolescents with a greater incidence noted in older adolescents. Aberrations in primordial germ cell proliferation/differentiation can lead to a variety of neoplasms, including teratomas, embryonal carcinoma, choriocarcinoma, and yolk sac tumors. PATIENTS AND METHODS: Three Finnish families with varying familial germ cell tumors were identified, and whole-genome sequencing was performed using an Illumina sequencing platform. In total, 22 unique subjects across the three families were sequenced. Family 1 proband (female) was affected by malignant ovarian teratoma, Family 2 proband (female) was affected by sacrococcygeal teratoma with yolk sac tumor in the setting of Cornelia de Lange syndrome, and Family 3 proband (male) was affected by malignant testicular teratoma. Rare variants were identified using an autosomal recessive or de novo model of inheritance. RESULTS: For family 1 proband (female), an autosomal recessive or de novo model of inheritance identified variants of interest in the following genes: CD109, IKBKB, and CTNNA3, SUPT6H, MUC5AC, and FRG1. Family 2 proband (female) analysis identified gene variants of interest in the following genes: LONRF2, ANO7, HS6ST1, PRB2, and DNM2. Family 3 proband (male) analysis identified the following potential genes: CRIPAK, KRTAP5-7, and CACNA1B. CONCLUSION: Leveraging deep pedigrees and next-generation sequencing, rare germline variants were identified that were enriched in three families from Finland with a history of familial germ cell tumors. The data presented support the importance of germline mutations when analyzing complex cancers with a low somatic mutation landscape.

Strong concordance between RNA structural and single nucleotide variants identified via next generation sequencing techniques in primary pediatric leukemia and patient-derived xenograft samples.

Acute leukemia represents the most common pediatric malignancy comprising diverse subtypes with varying prognosis and treatment outcomes. New and targeted treatment options are warranted for this disease. Patient-derived xenograft (PDX) models are increasingly being used for preclinical testing of novel treatment modalities. A novel approach involving targeted error-corrected RNA sequencing using ArcherDX HemeV2 kit was employed to compare 25 primary pediatric acute leukemia samples and their corresponding PDX samples. A comparison of the primary samples and PDX samples revealed a high concordance between single nucleotide variants and gene fusions whereas other complex structural variants were not as consistent. The presence of gene fusions representing the major driver mutations at similar allelic frequencies in PDX samples compared to primary samples and over multiple passages confirms the utility of PDX models for preclinical drug testing. Characterization and tracking of these novel cryptic fusions and exonal variants in PDX models is critical in assessing response to potential new therapies.

Accelerating next generation sequencing data analysis: an evaluation of optimized best practices for Genome Analysis Toolkit algorithms.

Advancements in next generation sequencing (NGS) technologies have significantly increased the translational use of genomics data in the medical field as well as the demand for computational infrastructure capable processing that data. To enhance the current understanding of software and hardware used to compute large scale human genomic datasets (NGS), the performance and accuracy of optimized versions of GATK algorithms, including Parabricks and Sentieon, were compared to the results of the original application (GATK V4.1.0, Intel x86 CPUs). Parabricks was able to process a 50× whole-genome sequencing library in under 3 h and Sentieon finished in under 8 h, whereas GATK v4.1.0 needed nearly 24 h. These results were achieved while maintaining greater than 99% accuracy and precision compared to stock GATK. Sentieon's somatic pipeline achieved similar results greater than 99%. Additionally, the IBM POWER9 CPU performed well on bioinformatic workloads when tested with 10 different tools for alignment/mapping.

Error-corrected sequencing strategies enable comprehensive detection of leukemic mutations relevant for diagnosis and minimal residual disease monitoring.

BACKGROUND: Pediatric leukemias have a diverse genomic landscape associated with complex structural variants, including gene fusions, insertions and deletions, and single nucleotide variants. Routine karyotype and fluorescence in situ hybridization (FISH) techniques lack sensitivity for smaller genomic alternations. Next-generation sequencing (NGS) assays are being increasingly utilized for assessment of these various lesions. However, standard NGS lacks quantitative sensitivity for minimal residual disease (MRD) surveillance due to an inherently high error rate. METHODS: Primary bone marrow samples from pediatric leukemia (n = 32) and adult leukemia subjects (n = 5), cell line MV4-11, and an umbilical cord sample were utilized for this study. Samples were sequenced using molecular barcoding with targeted DNA and RNA library enrichment techniques based on anchored multiplexed PCR (AMP®) technology, amplicon based error-corrected sequencing (ECS) or a human cancer transcriptome assay. Computational analyses were performed to quantitatively assess limit of detection (LOD) for various DNA and RNA lesions, which could be systematically used for MRD assays. RESULTS: Matched leukemia patient samples were analyzed at three time points; diagnosis, end of induction (EOI), and relapse. Similar to flow cytometry for ALL MRD, the LOD for point mutations by these sequencing strategies was ≥0.001. For DNA structural variants, FLT3 internal tandem duplication (ITD) positive cell line and patient samples showed a LOD of ≥0.001 in addition to previously unknown copy number losses in leukemia genes. ECS in RNA identified multiple novel gene fusions, including a SPANT-ABL gene fusion in an ALL patient, which could have been used to alter therapy. Collectively, ECS for RNA demonstrated a quantitative and complex landscape of RNA molecules with 12% of the molecules representing gene fusions, 12% exon duplications, 8% exon deletions, and 68% with retained introns. Droplet digital PCR validation of ECS-RNA confirmed results to single mRNA molecule quantities. CONCLUSIONS: Collectively, these assays enable a highly sensitive, comprehensive, and simultaneous analysis of various clonal leukemic mutations, which can be tracked across disease states (diagnosis, EOI, and relapse) with a high degree of sensitivity. The approaches and results presented here highlight the ability to use NGS for MRD tracking.

Perspectives on Molecular Diagnostic Testing for the COVID-19 Pandemic in Delaware.

The United States has quickly transitioned into one of the epicenters for the coronavirus pandemic. Limitations for rapid testing for the virus responsible for the pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the single most important barrier for early detection and prevention of future outbreaks. Combining innovative molecular biology techniques, such as clustered regularly interspaced short palindromic repeats (CRISPR)/Cas nuclease systems and next generation sequencing (NGS) may prove to be an effective solution to establish a high-throughput diagnostic and genomic surveillance workflow for COVID-19 in the State of Delaware. Integrating key expertise across the medical institutions in Delaware, including ChristianaCare and Nemours/Alfred I. duPont Hospital for Children, is one potential solution for overcoming current barriers and driving a successful implementation of these techniques.

Mapping Citrullinated Sites in Multiple Organs of Mice Using Hypercitrullinated Library.

Protein citrullination (or deimination), an irreversible post-translational modification, has been implicated in several physiological and pathological processes, including gene expression regulation, apoptosis, rheumatoid arthritis, and Alzheimer's disease. Several research studies have been carried out on citrullination under many conditions. However, until now, challenges in sample preparation and data analysis have made it difficult to confidently identify a citrullinated protein and assign the citrullinated site. To overcome these limitations, we generated a mouse hyper-citrullinated spectral library and set up coordinates to confidently identify and validate citrullinated sites. Using this workflow, we detect a four-fold increase in citrullinated proteome coverage across six mouse organs compared with the current state-of-the art techniques. Our data reveal that the subcellular distribution of citrullinated proteins is tissue-type-dependent and that citrullinated targets are involved in fundamental physiological processes, including the metabolic process. These data represent the first report of a hyper-citrullinated library for the mouse and serve as a central resource for exploring the role of citrullination in this organism.

Development of a Novel Next-Generation Sequencing Assay for Carrier Screening in Old Order Amish and Mennonite Populations of Pennsylvania.

Genetically isolated populations, such as the Old Order Amish and Old Order Mennonite communities, have an increased incidence of specific autosomal recessive disorders caused by the founder effect. In these populations, robust expanded carrier screening and diagnostic testing have the potential to reduce overall medical costs and improve patient outcomes. A novel next-generation sequencing assay was developed using anchored multiplex PCR technology (ArcherDX) for 162 different genetic syndromes caused by 202 pathogenic variants consisting of 150 single-nucleotide changes, 43 small insertion/deletions, and 9 large deletions (>20 nucleotides). To assess the accuracy of the screening panel results, 48 samples were selected on the basis of prior whole exome sequencing results. An additional 15 samples were chosen specifically to validate SMN1 and SMN2 copy number analyses. Collectively, the screening panel detected 273 pathogenic single-nucleotide or small insertion/deletion variants, 35 copy number variations, and 1 chromosomal abnormality (Klinefelter syndrome). Concordance with prior whole exome sequencing was 100%. By using a novel next-generation sequencing workflow, a successful targeted gene variant panel was developed for the Old Order Amish and Old Order Mennonite populations of Lancaster County, Pennsylvania. Population-wide carrier screening may help decrease the morbidity and mortality of these conditions in the high-risk populations.

Whole genome metagenomic analysis of the gut microbiome of differently fed infants identifies differences in microbial composition and functional genes, including an absent CRISPR/Cas9 gene in the formula-fed cohort.

BACKGROUND: Advancements in sequencing capabilities have enhanced the study of the human microbiome. There are limited studies focused on the gastro-intestinal (gut) microbiome of infants, particularly the impact of diet between breast-fed (BF) versus formula-fed (FF). It is unclear what effect, if any, early feeding has on short-term or long-term composition and function of the gut microbiome. RESULTS: Using a shotgun metagenomics approach, differences in the gut microbiome between BF (n = 10) and FF (n = 5) infants were detected. A Jaccard distance principle coordinate analysis was able to cluster BF versus FF infants based on the presence or absence of species identified in their gut microbiome. Thirty-two genera were identified as statistically different in the gut microbiome sequenced between BF and FF infants. Furthermore, the computational workflow identified 371 bacterial genes that were statistically different between the BF and FF cohorts in abundance. Only seven genes were lower in abundance (or absent) in the FF cohort compared to the BF cohort, including CRISPR/Cas9; whereas, the remaining candidates, including autotransporter adhesins, were higher in abundance in the FF cohort compared to BF cohort. CONCLUSIONS: These studies demonstrated that FF infants have, at an early age, a significantly different gut microbiome with potential implications for function of the fecal microbiota. Interactions between the fecal microbiota and host hinted at here have been linked to numerous diseases. Determining whether these non-abundant or more abundant genes have biological consequence related to infant feeding may aid in understanding the adult gut microbiome, and the pathogenesis of obesity.