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ABSTRACTSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had an unprecedented impact on human health and highlights the need for genomic epidemiology studies to increase our understanding of virus evolution and spread and to inform policy decisions. We sequenced viral genomes from over 22,000 patient samples tested at Mayo Clinic Laboratories between 2020 and 2022 and used Bayesian phylodynamics to describe county and regional spread in Minnesota. The earliest calculated introduction into Minnesota was to Hennepin County from a domestic source around 22 January 2020; 6 weeks before the first confirmed case in the state. This led to the virus spreading to Northern Minnesota and, eventually, the rest of the state. International introductions were most abundant in Hennepin (home to the Minneapolis/St. Paul International Airport) totaling 45 (out of 107) over the 2-year period. Southern Minnesota counties were most common for domestic introductions, with 19 (out of 64), potentially driven by bordering states such as Iowa and Wisconsin as well as Illinois, which is nearby. Hennepin also was, by far, the most dominant source of in-state transmissions to other Minnesota locations (n = 772) over the 2-year period. We also analyzed the diversity of the location source of SARS-CoV-2 viruses in each county and noted the timing of state-wide policies as well as trends in clinical cases. Neither the number of clinical cases nor the major policy decisions, such as the end of the lockdown period in 2020 or the end of all restrictions in 2021, appeared to have an impact on virus diversity across each individual county.IMPORTANCEWe analyzed over 22,000 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes of patient samples tested at Mayo Clinic Laboratories during a 2-year period in the COVID-19 pandemic, which included Alpha, Delta, and Omicron variants of concern to examine the roles and relationships of Minnesota virus transmission. We found that Hennepin County, the most populous county, drove the transmission of SARS-CoV-2 viruses in the state after including the formation of earlier clades including 20A, 20C, and 20G, as well as variants of concern Alpha and Delta. We also found that Hennepin County was the source for most of the county-to-county introductions after an initial predicted introduction with the virus in early 2020 from an international source, while other counties acted as transmission “sinks.” In addition, major policies, such as the end of the lockdown period in 2020 or the end of all restrictions in 2021, did not appear to have an impact on virus diversity across individual counties.

Following publication of the original article [1], the author explained that Table 2 is displayed incorrectly. The correct Table 2 is given below. The original article has been corrected.

As reliable, efficient genome sequencing becomes ubiquitous, the need for similarly reliable and efficient variant calling becomes increasingly important. The Genome Analysis Toolkit (GATK), maintained by the Broad Institute, is currently the widely accepted standard for variant calling software. However, alternative solutions may provide faster variant calling without sacrificing accuracy. One such alternative is Sentieon DNASeq, a toolkit analogous to GATK but built on a highly optimized backend. We conducted an independent evaluation of the DNASeq single-sample variant calling pipeline in comparison to that of GATK. Our results support the near-identical accuracy of the two software packages, showcase optimal scalability and great speed from Sentieon, and describe computational performance considerations for the deployment of DNASeq.

Abstract Background Use of the Genome Analysis Toolkit (GATK) continues to be the standard practice in genomic variant calling in both research and the clinic. Recently the toolkit has been rapidly evolving. Significant computational performance improvements have been introduced in GATK3.8 through collaboration with Intel in 2017. The first release of GATK4 in early 2018 revealed rewrites in the code base, as the stepping stone toward a Spark implementation. As the software continues to be a moving target for optimal deployment in highly productive environments, we present a detailed analysis of these improvements, to help the community stay abreast with changes in performance. Results We re-evaluated multiple options, such as threading, parallel garbage collection, I/O options and data-level parallelization. Additionally, we considered the trade-offs of using GATK3.8 and GATK4. We found optimized parameter values that reduce the time of executing the best practices variant calling procedure by 29.3% for GATK3.8 and 16.9% for GATK4. Further speedups can be accomplished by splitting data for parallel analysis, resulting in run time of only a few hours on whole human genome sequenced to the depth of 20X, for both versions of GATK. Nonetheless, GATK4 is already much more cost-effective than GATK3.8. Thanks to significant rewrites of the algorithms, the same analysis can be run largely in a single-threaded fashion, allowing users to process multiple samples on the same CPU. Conclusions In time-sensitive situations, when a patient has a critical or rapidly developing condition, it is useful to minimize the time to process a single sample. In such cases we recommend using GATK3.8 by splitting the sample into chunks and computing across multiple nodes. The resultant walltime will be nnn.4 hours at the cost of $41.60 on 4 c5.18xlarge instances of Amazon Cloud. For cost-effectiveness of routine analyses or for large population studies, it is useful to maximize the number of samples processed per unit time. Thus we recommend GATK4, running multiple samples on one node. The total walltime will be ∼34.1 hours on 40 samples, with 1.18 samples processed per hour at the cost of $2.60 per sample on c5.18xlarge instance of Amazon Cloud.

Primary cultures of human corneal endothelial cells (HCECs) are an important model system for studying the pathophysiology of corneal endothelium. The purpose of this study was to identify and validate an optimal primary culture model of normal and Fuchs endothelial corneal dystrophy (FECD) endothelial cells by comparing cell morphology and marker expression under different media conditions to in vivo donor tissues. Primary and immortalized HCECs, isolated from normal and FECD donors, were cultured in proliferation media (Joyce, M4, Bartakova) alone or sequentially with maturation media (F99, Stabilization 1, M5). CD56, CD73 and CD166 expressions were quantified in confluent and matured cell lines by flow cytometry. HCECs that were allowed to proliferate in Joyce's medium followed by maturation in low-mitogen containing media yielded cells with similar morphology to corneal endothelial tissues. Elevated expression of CD56 and CD166 and low expression of CD73 correlated with regular, hexagonal-like HCEC morphology. CD56:CD73 > 2.5 was most consistent with normal HCEC morphology and mimicked corneal endothelial tissue. Immortalization of normal HCECs by hTERT transduction showed morphology and CD56:CD73 ratios similar to parental cell lines. HCECs established from FECD donors showed reduced CD56:CD73 ratios compared to normal HCECs which coincided with reduced uniformity and regularity of cell monolayers. Overall, a dual media system with Joyce's medium for proliferation and a low-mitogen media for maturation, provided normal cultures with regular, hexagonal-like cell morphologies consistent with corneal endothelial cells in vivo. CD56:CD73 expression ratio >2.5 was predictive of in vivo-like cellular morphology.

Expansion of CTG trinucleotide repeats (TNR) in the transcription factor 4 (TCF4) gene is highly associated with Fuchs Endothelial Corneal Dystrophy (FECD). Due to limitations in the availability of DNA from diseased corneal endothelium, sizing of CTG repeats in FECD patients has typically been determined using DNA samples isolated from peripheral blood leukocytes. However, it is non-feasible to extract enough DNA from surgically isolated FECD corneal endothelial tissue to determine repeat length based on current technology. To circumvent this issue, total RNA was isolated from FECD corneal endothelium and sequenced using long-read sequencing. Southern blotting of DNA samples isolated from primary cultures of corneal endothelium from these same affected individuals was also assessed. Both long read sequencing and Southern blot analysis showed significantly longer CTG TNR expansion (>1000 repeats) in the corneal endothelium from FECD patients than those characterized in leukocytes from the same individuals (

Amplification of a CAG trinucleotide motif (CTG18.1) within the TCF4 gene has been strongly associated with Fuchs Endothelial Corneal Dystrophy (FECD). Nevertheless, a small minority of clinically unaffected elderly patients who have expanded CTG18.1 sequences have been identified. To test the hypothesis that the CAG expansions in these patients are protected from FECD because they have interruptions within the CAG repeats, we utilized a combination of an amplification-free, long-read sequencing method and a new target-enrichment sequence analysis tool developed by Pacific Biosciences to interrogate the sequence structure of expanded repeats. The sequencing was successful in identifying a previously described interruption within an unexpanded allele and provided sequence data on expanded alleles greater than 2000 bases in length. The data revealed considerable heterogeneity in the size distribution of expanded repeats within each patient. Detailed analysis of the long sequence reads did not reveal any instances of interruptions to the expanded CAG repeats, but did reveal novel variants within the AGG repeats that flank the CAG repeats in two of the five samples from clinically unaffected patients with expansions. This first examination of the sequence structure of CAG repeats in CTG18.1 suggests that factors other than interruptions to the repeat structure account for the absence of disease in some elderly patients with repeat expansions in the TCF4 gene.

Fuchs Endothelial Corneal Dystrophy (FECD) is a late onset, autosomal dominant eye disease that can lead to loss of vision. Expansion of a CTG trinucleotide repeat in the third intron of the transcription factor 4 (TCF4) gene is highly associated with FECD. However, only about 75% of FECD patients in the northern European population possess an expansion of this repeat. The remaining FECD cases appear to be associated with variants in other genes. To better understand the pathophysiology of this disease, we compared gene expression profiles of corneal endothelium from FECD patients with an expanded trinucleotide repeat (RE+) to those that do not have a repeat expansion (RE-). Comparative analysis of these two cohorts showed widespread RNA mis-splicing in RE+, but not in RE- samples. Quantitatively, we identified 39 genes in which expression was significantly different between RE+ and RE- samples. Examination of the mutation profiles in the RE- samples did not find any mutations in genes previously associated with FECD, but did reveal one sample with a rare variant of laminin subunit gamma 1 (LAMC1) and three samples with rare variants in the gene coding for the mitochondrial protein peripheral-type benzodiazepine receptor-associated protein 1 (TSPOAP1).

Whole Exome Sequencing (WES) is increasingly being used for diagnosis without adequate information on predictive characteristics of reportable variants typically found on any given individual and correlation with clinical phenotype. In this study, we performed WES on 89 deceased individuals (mean age at death 74 years, range 28-93) from the Mayo Clinic Biobank. Significant clinical diagnoses were abstracted from electronic-medical-record via chart review. Variants (Single Nucleotide Variant and insertion/deletion) were filtered based on quality (accuracy >99%, read-depth >20, alternate-allele read-depth >5, minor-allele-frequency 0.19). Evaluating genotype-phenotype correlations across the exome, 202 (3%) of 7046 filtered variants had some evidence for phenotypic correlation in medical-records, while 3710 (53%) variants had no phenotypic correlation. The phenotype associated with the remaining 44% could not be assessed from a typical medical record review. These data highlight significant continued challenges in the ability to extract medically meaningful predictive results from WES.

Developing patient derived models from individual tumors that capture the biological heterogeneity and mutation landscape in advanced prostate cancer is challenging, but essential for understanding tumor progression and delivery of personalized therapy in metastatic castrate resistant prostate cancer stage. To demonstrate the feasibility of developing patient derived xenograft models in this stage, we present a case study wherein xenografts were derived from cancer metastases in a patient progressing on androgen deprivation therapy and prior to initiating pre-chemotherapy enzalutamide treatment. Tissue biopsies from a metastatic rib lesion were obtained for sequencing before and after initiating enzalutamide treatment over a twelve-week period and also implanted subcutaneously as well as under the renal capsule in immuno-deficient mice. The genome and transcriptome landscapes of xenografts and the original patient tumor tissues were compared by performing whole exome and transcriptome sequencing of the metastatic tumor tissues and the xenografts at both time points. After comparing the somatic mutations, copy number variations, gene fusions and gene expression we found that the patient's genomic and transcriptomic alterations were preserved in the patient derived xenografts with high fidelity. These xenograft models provide an opportunity for predicting efficacy of existing and potentially novel drugs that is based on individual metastatic tumor expression signature and molecular pharmacology for delivery of precision medicine.

Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp.

FKBP51, (FKBP5), is a negative regulator of Akt. Variability in FKBP5 expression level is a major factor contributing to variation in response to chemotherapeutic agents including gemcitabine, a first line treatment for pancreatic cancer. Genetic variation in FKBP5 could influence its function and, ultimately, treatment response of pancreatic cancer.We set out to comprehensively study the role of genetic variation in FKBP5 identified by Next Generation DNA resequencing on response to gemcitabine treatment of pancreatic cancer by utilizing both tumor and germline DNA samples from 43 pancreatic cancer patients, including 19 paired normal-tumor samples. Next, genotype-phenotype association studies were performed with overall survival as well as with FKBP5 gene expression in tumor using the same samples in which resequencing had been performed, followed by functional genomics studies.In-depth resequencing identified 404 FKBP5 single nucleotide polymorphisms (SNPs) in normal and tumor DNA. SNPs with the strongest associations with survival or FKBP5 expression were subjected to functional genomic study. Electromobility shift assay showed that the rs73748206 "A(T)" SNP altered DNA-protein binding patterns, consistent with significantly increased reporter gene activity, possibly through its increased binding to Glucocorticoid Receptor (GR). The effect of rs73748206 was confirmed on the basis of its association with FKBP5 expression by affecting the binding to GR in lymphoblastoid cell lines derived from the same patients for whom DNA was used for resequencing.This comprehensive FKBP5 resequencing study provides insights into the role of genetic variation in variation of gemcitabine response.

MicroRNAs play a role in regulating diverse biological processes and have considerable utility as molecular markers for diagnosis and monitoring of human disease. Several technologies are available commercially for measuring microRNA expression. However, cross-platform comparisons do not necessarily correlate well, making it difficult to determine which platform most closely represents the true microRNA expression level in a tissue. To address this issue, we have analyzed RNA derived from cell lines, as well as fresh frozen and formalin-fixed paraffin embedded tissues, using Affymetrix, Agilent, and Illumina microRNA arrays, NanoString counting, and Illumina Next Generation Sequencing. We compared the performance within- and between the different platforms, and then verified these results with those of quantitative PCR data. Our results demonstrate that the within-platform reproducibility for each method is consistently high and although the gene expression profiles from each platform show unique traits, comparison of genes that were commonly detectable showed that detection of microRNA transcripts was similar across multiple platforms.

Fuchs endothelial corneal dystrophy (FECD) is a common, familial disease of the corneal endothelium and is the leading indication for corneal transplantation. Variation in the transcription factor 4 (TCF4) gene has been identified as a major contributor to the disease. We tested for an association between an intronic TGC trinucleotide repeat in TCF4 and FECD by determining repeat length in 66 affected participants with severe FECD and 63 participants with normal corneas in a 3-stage discovery/replication/validation study. PCR primers flanking the TGC repeat were used to amplify leukocyte-derived genomic DNA. Repeat length was determined by direct sequencing, short tandem repeat (STR) assay and Southern blotting. Genomic Southern blots were used to evaluate samples for which only a single allele was identified by STR analysis. Compiling data for 3 arms of the study, a TGC repeat length >50 was present in 79% of FECD cases and in 3% of normal controls cases (p50 TGC repeats, 13 (20%) had 50 repeats, 60 (95%) had 50 TGC repeats identifying FECD in this patient cohort was 79% and 96%, respectively Expanded TGC repeat was more specific for FECD cases than the previously identified, highly associated, single nucleotide polymorphism, rs613872 (specificity = 79%). The TGC trinucleotide repeat expansion in TCF4 is strongly associated with FECD, and a repeat length >50 is highly specific for the disease This association suggests that trinucleotide expansion may play a pathogenic role in the majority of FECD cases and is a predictor of disease risk.