Ancestry-based differences in the immune phenotype are associated with lupus activity.

Systemic lupus erythematosus (SLE) affects 1 in 537 Black women, which is >2-fold more than White women. Black patients develop the disease at a younger age, have more severe symptoms, and have a greater chance of early mortality. We used a multiomics approach to uncover ancestry-associated immune alterations in patients with SLE and healthy controls that may contribute biologically to disease disparities. Cell composition, signaling, epigenetics, and proteomics were evaluated by mass cytometry; droplet-based single-cell transcriptomics and proteomics; and bead-based multiplex soluble mediator levels in plasma. We observed altered whole blood frequencies and enhanced activity in CD8+ T cells, B cells, monocytes, and DCs in Black patients with more active disease. Epigenetic modifications in CD8+ T cells (H3K27ac) could distinguish disease activity level in Black patients and differentiate Black from White patient samples. TLR3/4/7/8/9-related gene expression was elevated in immune cells from Black patients with SLE, and TLR7/8/9 and IFN-α phospho-signaling and cytokine responses were heightened even in immune cells from healthy Black control patients compared with White individuals. TLR stimulation of healthy immune cells recapitulated the ancestry-associated SLE immunophenotypes. This multiomic resource defines ancestry-associated immune phenotypes that differ between Black and White patients with SLE, which may influence the course and severity of SLE and other diseases.

Inferring direction of associations between histone modifications using a neural processes-based framework.

Current technologies do not allow predicting interactions between histone post-translational modifications (HPTMs) at a system-level. We describe a computational framework, imputation-followed-by-inference, to predict directed association between two HPTMs using EpiTOF, a mass cytometry-based platform that allows profiling multiple HPTMs at a single-cell resolution. Using EpiTOF profiles of >55,000,000 peripheral mononuclear blood cells from 158 healthy human subjects, we show that neural processes (NP) have significantly higher accuracy than linear regression and k-nearest neighbors models to impute the abundance of an HPTM. Next, we infer the direction of association to show we recapitulate known HPTM associations and identify several previously unidentified ones in healthy individuals. Using this framework in an influenza vaccine cohort, we identify changes in associations between 6 pairs of HPTMs 30 days following vaccination, of which several have been shown to be involved in innate memory. These results demonstrate the utility of our framework in identifying directed interactions between HPTMs.

Mass-Cytometry-Based Quantification of Global Histone Post-Translational Modifications at Single-Cell Resolution Across Peripheral Immune Cells in IBD.

BACKGROUND AND AIMS: Current understanding of histone post-translational modifications [histone modifications] across immune cell types in patients with inflammatory bowel disease [IBD] during remission and flare is limited. The present study aimed to quantify histone modifications at a single-cell resolution in IBD patients during remission and flare and how they differ compared to healthy controls. METHODS: We performed a case-control study of 94 subjects [83 IBD patients and 11 healthy controls]. IBD patients had either ulcerative colitis [n = 38] or Crohn's disease [n = 45] in clinical remission or flare. We used epigenetic profiling by time-of-flight [EpiTOF] to investigate changes in histone modifications within peripheral blood mononuclear cells from IBD patients. RESULTS: We discovered substantial heterogeneity in histone modifications across multiple immune cell types in IBD patients. They had a higher proportion of less differentiated CD34+ haematopoietic progenitors, and a subset of CD56bright natural killer [NK] cells and γδ T cells characterized by distinct histone modifications associated with gene transcription. The subset of CD56bright NK cells had increases in several histone acetylations. An epigenetically defined subset of NK cells was associated with higher levels of C-reactive protein in peripheral blood. CD34+ monocytes from IBD patients had significantly decreased cleaved H3T22, suggesting they were epigenetically primed for macrophage differentiation. CONCLUSION: We describe the first systems-level quantification of histone modifications across immune cells from IBD patients at a single-cell resolution, revealing the increased epigenetic heterogeneity that is not possible with traditional ChIP-seq profiling. Our data open new directions in investigating the association between histone modifications and IBD pathology using other epigenomic tools.

Increasing reproducibility, robustness, and generalizability of biomarker selection from meta-analysis using Bayesian methodology.

A major limitation of gene expression biomarker studies is that they are not reproducible as they simply do not generalize to larger, real-world, heterogeneous populations. Frequentist multi-cohort gene expression meta-analysis has been frequently used as a solution to this problem to identify biomarkers that are truly differentially expressed. However, the frequentist meta-analysis framework has its limitations-it needs at least 4-5 datasets with hundreds of samples, is prone to confounding from outliers and relies on multiple-hypothesis corrected p-values. To address these shortcomings, we have created a Bayesian meta-analysis framework for the analysis of gene expression data. Using real-world data from three different diseases, we show that the Bayesian method is more robust to outliers, creates more informative estimates of between-study heterogeneity, reduces the number of false positive and false negative biomarkers and selects more generalizable biomarkers with less data. We have compared the Bayesian framework to a previously published frequentist framework and have developed a publicly available R package for use.

Computational drug repositioning of atorvastatin for ulcerative colitis.

OBJECTIVE: Ulcerative colitis (UC) is a chronic inflammatory disorder with limited effective therapeutic options for long-term treatment and disease maintenance. We hypothesized that a multi-cohort analysis of independent cohorts representing real-world heterogeneity of UC would identify a robust transcriptomic signature to improve identification of FDA-approved drugs that can be repurposed to treat patients with UC. MATERIALS AND METHODS: We performed a multi-cohort analysis of 272 colon biopsy transcriptome samples across 11 publicly available datasets to identify a robust UC disease gene signature. We compared the gene signature to in vitro transcriptomic profiles induced by 781 FDA-approved drugs to identify potential drug targets. We used a retrospective cohort study design modeled after a target trial to evaluate the protective effect of predicted drugs on colectomy risk in patients with UC from the Stanford Research Repository (STARR) database and Optum Clinformatics DataMart. RESULTS: Atorvastatin treatment had the highest inverse-correlation with the UC gene signature among non-oncolytic FDA-approved therapies. In both STARR (n = 827) and Optum (n = 7821), atorvastatin intake was significantly associated with a decreased risk of colectomy, a marker of treatment-refractory disease, compared to patients prescribed a comparator drug (STARR: HR = 0.47, P = .03; Optum: HR = 0.66, P = .03), irrespective of age and length of atorvastatin treatment. DISCUSSION & CONCLUSION: These findings suggest that atorvastatin may serve as a novel therapeutic option for ameliorating disease in patients with UC. Importantly, we provide a systematic framework for integrating publicly available heterogeneous molecular data with clinical data at a large scale to repurpose existing FDA-approved drugs for a wide range of human diseases.

Machine learning for initial insulin estimation in hospitalized patients.

OBJECTIVE: The study sought to determine whether machine learning can predict initial inpatient total daily dose (TDD) of insulin from electronic health records more accurately than existing guideline-based dosing recommendations. MATERIALS AND METHODS: Using electronic health records from a tertiary academic center between 2008 and 2020 of 16,848 inpatients receiving subcutaneous insulin who achieved target blood glucose control of 100-180 mg/dL on a calendar day, we trained an ensemble machine learning algorithm consisting of regularized regression, random forest, and gradient boosted tree models for 2-stage TDD prediction. We evaluated the ability to predict patients requiring more than 6 units TDD and their point-value TDDs to achieve target glucose control. RESULTS: The method achieves an area under the receiver-operating characteristic curve of 0.85 (95% confidence interval [CI], 0.84-0.87) and area under the precision-recall curve of 0.65 (95% CI, 0.64-0.67) for classifying patients who require more than 6 units TDD. For patients requiring more than 6 units TDD, the mean absolute percent error in dose prediction based on standard clinical calculators using patient weight is in the range of 136%-329%, while the regression model based on weight improves to 60% (95% CI, 57%-63%), and the full ensemble model further improves to 51% (95% CI, 48%-54%). DISCUSSION: Owingto the narrow therapeutic window and wide individual variability, insulin dosing requires adaptive and predictive approaches that can be supported through data-driven analytic tools. CONCLUSIONS: Machine learning approaches based on readily available electronic medical records can discriminate which inpatients will require more than 6 units TDD and estimate individual doses more accurately than standard guidelines and practices.

SNPs2ChIP: Latent Factors of ChIP-seq to infer functions of non-coding SNPs.

Genetic variations of the human genome are linked to many disease phenotypes. While whole-genome sequencing and genome-wide association studies (GWAS) have uncovered a number of genotype-phenotype associations, their functional interpretation remains challenging given most single nucleotide polymorphisms (SNPs) fall into the non-coding region of the genome. Advances in chromatin immunoprecipitation sequencing (ChIP-seq) have made large-scale repositories of epigenetic data available, allowing investigation of coordinated mechanisms of epigenetic markers and transcriptional regulation and their influence on biological function. To address this, we propose SNPs2ChIP, a method to infer biological functions of non-coding variants through unsupervised statistical learning methods applied to publicly-available epigenetic datasets. We systematically characterized latent factors by applying singular value decomposition to ChIP-seq tracks of lymphoblastoid cell lines, and annotated the biological function of each latent factor using the genomic region enrichment analysis tool. Using these annotated latent factors as reference, we developed SNPs2ChIP, a pipeline that takes genomic region(s) as an input, identifies the relevant latent factors with quantitative scores, and returns them along with their inferred functions. As a case study, we focused on systemic lupus erythematosus and demonstrated our method's ability to infer relevant biological function. We systematically applied SNPs2ChIP on publicly available datasets, including known GWAS associations from the GWAS catalogue and ChIP-seq peaks from a previously published study. Our approach to leverage latent patterns across genome-wide epigenetic datasets to infer the biological function will advance understanding of the genetics of human diseases by accelerating the interpretation of non-coding genomes.

S-plot2: Rapid Visual and Statistical Analysis of Genomic Sequences.

With the daily release of data from whole genome sequencing projects, tools to facilitate comparative studies are hard-pressed to keep pace. Graphical software solutions can readily recognize synteny by measuring similarities between sequences. Nevertheless, regions of dissimilarity can prove to be equally informative; these regions may harbor genes acquired via lateral gene transfer (LGT), signify gene loss or gain, or include coding regions under strong selection. Previously, we developed the software S-plot. This tool employed an alignment-free approach for comparing bacterial genomes and generated a heatmap representing the genomes' similarities and dissimilarities in nucleotide usage. In prior studies, this tool proved valuable in identifying genome rearrangements as well as exogenous sequences acquired via LGT in several bacterial species. Herein, we present the next generation of this tool, S-plot2. Similar to its predecessor, S-plot2 creates an interactive, 2-dimensional heatmap capturing the similarities and dissimilarities in nucleotide usage between genomic sequences (partial or complete). This new version, however, includes additional metrics for analysis, new reporting options, and integrated BLAST query functionality for the user to interrogate regions of interest. Furthermore, S-plot2 can evaluate larger sequences, including whole eukaryotic chromosomes. To illustrate some of the applications of the tool, 2 case studies are presented. The first examines strain-specific variation across the Pseudomonas aeruginosa genome and strain-specific LGT events. In the second case study, corresponding human, chimpanzee, and rhesus macaque autosomes were studied and lineage specific contributions to divergence were estimated. S-plot2 provides a means to both visually and quantitatively compare nucleotide sequences, from microbial genomes to eukaryotic chromosomes. The case studies presented illustrate just 2 potential applications of the tool, highlighting its capability to identify and investigate the variation in molecular divergence rates across sequences. S-plot2 is freely available through https://bitbucket.org/lkalesinskas/splot and is supported on the Linux and MS Windows operating systems.

Draft Genome Sequence of Micrococcus luteus (Schroeter) Cohn (ATCC 12698).

The actinobacterium Micrococcus luteus can be found in a wide variety of habitats. Here, we report the 2,411,958-bp draft genome sequence of the type strain M. leuteus (Schroeter) Cohn (ATCC 12698). Characteristic of this taxa, the genome sequence has a high G+C content, 73.14%.

Draft Genome Sequence of Enterococcus faecalis ATCC BAA-2128.

While a part of the native gut microflora, the Gram-positive bacterium Enterococcus faecalis can lead to serious infections elsewhere in the body. The draft genome of E. faecalis strain ATCC BAA-2128, isolated from piglet feces, was examined. This draft genome consists of 42 contigs, 12 of which exhibit homology to annotated plasmids.

Draft Genome Sequences of Two ATCC Staphylococcus aureus subsp. aureus Strains.

Draft genome sequences for Staphylococcus aureus subsp. aureus Rosenbach ATCC 14458 and ATCC 27217 strains were investigated. The genome sizes were 2,880,761 bp and 2,759,100 bp, respectively. Strain ATCC 14458 was assembled into 39 contigs, including 3 plasmids, and strain ATCC 27217 was assembled into 25 contigs, including 2 plasmids.

Draft Genome Sequence of Staphylococcus epidermidis (Winslow and Winslow) Evans (ATCC 14990).

Here, we report the draft genome sequence for the type strain Staphylococcus epidermidis (Winslow and Winslow) Evans (ATCC 14990). The assembly consisted of 2,457,519 bp with an observed G+C content of 32.04%. Thirty-seven contigs were produced, including two putative plasmids, with a 296.8× coverage and an N50 of 180,848 bp.

Bayesian Analysis of Perceived Eye Level.

To accurately perceive the world, people must efficiently combine internal beliefs and external sensory cues. We introduce a Bayesian framework that explains the role of internal balance cues and visual stimuli on perceived eye level (PEL)-a self-reported measure of elevation angle. This framework provides a single, coherent model explaining a set of experimentally observed PEL over a range of experimental conditions. Further, it provides a parsimonious explanation for the additive effect of low fidelity cues as well as the averaging effect of high fidelity cues, as also found in other Bayesian cue combination psychophysical studies. Our model accurately estimates the PEL and explains the form of previous equations used in describing PEL behavior. Most importantly, the proposed Bayesian framework for PEL is more powerful than previous behavioral modeling; it permits behavioral estimation in a wider range of cue combination and perceptual studies than models previously reported.

Draft Genome Sequence of a Urinary Isolate of Lactobacillus crispatus.

While Lactobacillus crispatus contributes to the stability of normal vaginal microbiota, its role in urinary health remains unclear. As part of an on-going attempt to characterize the female urinary microbiota, we report the genome sequence of an L. crispatus strain isolated from a woman displaying no lower urinary tract symptoms.

Genome sequences and annotation of two urinary isolates of E. coli.

The genus Escherichia includes pathogens and commensals. Bladder infections (cystitis) result most often from colonization of the bladder by uropathogenic E. coli strains. In contrast, a poorly defined condition called asymptomatic bacteriuria results from colonization of the bladder with E. coli strains without symptoms. As part of an on-going attempt to identify and characterize the newly discovered female urinary microbiota, we report the genome sequences and annotation of two urinary isolates of E. coli: one (E78) was isolated from a female patient who self-reported cystitis; the other (E75) was isolated from a female patient who reported that she did not have symptoms of cystitis. Whereas strain E75 is most closely related to an avian extraintestinal pathogen, strain E78 is a member of a clade that includes extraintestinal strains often found in the human bladder. Both genomes are uncommonly rich in prophages.