Integrating population and single-cell variations in vaccine responses identifies a naturally adjuvanted human immune setpoint.

Multimodal single-cell profiling methods can capture immune cell variations unfolding over time at the molecular, cellular, and population levels. Transforming these data into biological insights remains challenging. Here, we introduce a framework to integrate variations at the human population and single-cell levels in vaccination responses. Comparing responses following AS03-adjuvanted versus unadjuvanted influenza vaccines with CITE-seq revealed AS03-specific early (day 1) response phenotypes, including a B cell signature of elevated germinal center competition. A correlated network of cell-type-specific transcriptional states defined the baseline immune status associated with high antibody responders to the unadjuvanted vaccine. Certain innate subsets in the network appeared "naturally adjuvanted," with transcriptional states resembling those induced uniquely by AS03-adjuvanted vaccination. Consistently, CD14+ monocytes from high responders at baseline had elevated phospho-signaling responses to lipopolysaccharide stimulation. Our findings link baseline immune setpoints to early vaccine responses, with positive implications for adjuvant development and immune response engineering.

Calibration of variant effect predictors on genome-wide data masks heterogeneous performance across genes.

In silico variant effect predictions are available for nearly all missense variants but played a minimal role in clinical variant classification because they were deemed to provide only supporting evidence. Recently, the ClinGen Sequence Variant Interpretation (SVI) Working Group updated recommendations for variant effect prediction use. By analyzing control pathogenic and benign variants across all genes, they were able to compute evidence strength for predictor score intervals with some intervals generating moderate, strong, or even very strong evidence. However, this genome-wide approach could obscure heterogeneous predictor performance in different genes. We quantified the gene-by-gene performance of two top predictors, REVEL and BayesDel, by analyzing control variants in each predictor score interval in 3,668 disease-relevant genes. Approximately 10% of intervals had sufficient control variants for analysis, and ∼70% of these intervals exceeded the maximum number of incorrect predictions implied by the SVI recommendations. These trending discordant intervals arose owing to the divergence of the gene-specific distribution of predictions from the genome-wide distribution, suggesting that gene-specific calibration is needed in many cases. Approximately 22% of ClinVar missense variants of uncertain significance in genes we analyzed (REVEL = 100,629, BayesDel = 71,928) had predictions in trending discordant intervals. Thus, genome-wide calibrations could result in many variants receiving inappropriate evidence strength. To facilitate a review of the SVI's calibrations, we developed a web application enabling visualization of gene-specific predictions and trending concordant and discordant intervals.

Predictors of Inflammation-Mediated Preterm Birth.

Preterm birth remains a worldwide health concern due to ongoing challenges in prediction and prevention. Current predictors are limited by poor performance, need for invasive sampling, and an inability to identify patients in a timely fashion to allow for effective intervention. The multiple etiologies of preterm birth often have an inflammatory component. Thus, a deeper understanding of the inflammatory mechanisms involved in preterm birth may provide opportunities to identify new predictors of preterm birth. This review will discuss the multiple etiologies of preterm birth, their links to inflammation, current predictors available, and new directions for the field.

Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria.

Recommendations from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) for interpreting sequence variants specify the use of computational predictors as "supporting" level of evidence for pathogenicity or benignity using criteria PP3 and BP4, respectively. However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support. Previously, we described a probabilistic framework that quantified the strengths of evidence (supporting, moderate, strong, very strong) within ACMG/AMP recommendations. We have extended this framework to computational predictors and introduce a new standard that converts a tool's scores to PP3 and BP4 evidence strengths. Our approach is based on estimating the local positive predictive value and can calibrate any computational tool or other continuous-scale evidence on any variant type. We estimate thresholds (score intervals) corresponding to each strength of evidence for pathogenicity and benignity for thirteen missense variant interpretation tools, using carefully assembled independent data sets. Most tools achieved supporting evidence level for both pathogenic and benign classification using newly established thresholds. Multiple tools reached score thresholds justifying moderate and several reached strong evidence levels. One tool reached very strong evidence level for benign classification on some variants. Based on these findings, we provide recommendations for evidence-based revisions of the PP3 and BP4 ACMG/AMP criteria using individual tools and future assessment of computational methods for clinical interpretation.

penalizedclr: an R package for penalized conditional logistic regression for integration of multiple omics layers.

BACKGROUND: The matched case-control design, up until recently mostly pertinent to epidemiological studies, is becoming customary in biomedical applications as well. For instance, in omics studies, it is quite common to compare cancer and healthy tissue from the same patient. Furthermore, researchers today routinely collect data from various and variable sources that they wish to relate to the case-control status. This highlights the need to develop and implement statistical methods that can take these tendencies into account. RESULTS: We present an R package penalizedclr, that provides an implementation of the penalized conditional logistic regression model for analyzing matched case-control studies. It allows for different penalties for different blocks of covariates, and it is therefore particularly useful in the presence of multi-source omics data. Both L1 and L2 penalties are implemented. Additionally, the package implements stability selection for variable selection in the considered regression model. CONCLUSIONS: The proposed method fills a gap in the available software for fitting high-dimensional conditional logistic regression models accounting for the matched design and block structure of predictors/features. The output consists of a set of selected variables that are significantly associated with case-control status. These variables can then be investigated in terms of functional interpretation or validation in further, more targeted studies.

A Systematic Review of Predictor Composition, Outcomes, Risk of Bias, and Validation of COVID-19 Prognostic Scores.

BACKGROUND: Numerous prognostic scores have been published to support risk stratification for patients with coronavirus disease 2019 (COVID-19). METHODS: We performed a systematic review to identify the scores for confirmed or clinically assumed COVID-19 cases. An in-depth assessment and risk of bias (ROB) analysis (Prediction model Risk Of Bias ASsessment Tool [PROBAST]) was conducted for scores fulfilling predefined criteria ([I] area under the curve [AUC)] ≥ 0.75; [II] a separate validation cohort present; [III] training data from a multicenter setting [≥2 centers]; [IV] point-scale scoring system). RESULTS: Out of 1522 studies extracted from MEDLINE/Web of Science (20/02/2023), we identified 242 scores for COVID-19 outcome prognosis (mortality 109, severity 116, hospitalization 14, long-term sequelae 3). Most scores were developed using retrospective (75.2%) or single-center (57.1%) cohorts. Predictor analysis revealed the primary use of laboratory data and sociodemographic information in mortality and severity scores. Forty-nine scores were included in the in-depth analysis. The results indicated heterogeneous quality and predictor selection, with only five scores featuring low ROB. Among those, based on the number and heterogeneity of validation studies, only the 4C Mortality Score can be recommended for clinical application so far. CONCLUSIONS: The application and translation of most existing COVID scores appear unreliable. Guided development and predictor selection would have improved the generalizability of the scores and may enhance pandemic preparedness in the future.

Clinical Predictors and Outcomes of Invasive Anal Cancer for People With HIV in an Inception Cohort.

BACKGROUND: Due to the heterogeneity of risk for invasive anal cancer (IAC) among people with human immunodeficiency virus (PWH), we investigated predictors of IAC and described outcomes among those with a cancer diagnosis. METHODS: Using a longitudinal inception cohort of anal cancer screening, we evaluated risk factors and outcome probabilities for incident IAC in Cox models. Screening included anal cytology and digital anorectal examination, and, if results of either were abnormal, high-resolution anoscopy. RESULTS: Between 30 November 2006 and 3 March 2021, a total of 8139 PWH received care at the University of California, San Diego, with 4105 individuals undergoing screening and subsequently followed up over a median of 5.5 years. Anal cancer developed in 33 of them. IAC was more likely to develop in patients with anal high-grade squamous intraepithelial lesions (aHSILs) on initial or subsequent follow-up cytology (hazard ratio, 4.54) and a nadir CD4 cell count ≤200/µL (2.99). The joint effect of aHSILs and nadir CD4 cell count ≤200/µL amplified the hazard of IAC by 9-fold compared with the absence of both. PWH with time-updated cytology aHSIL and CD4 cell counts ≤200/µL had 5- and 10-year probabilities of IAC of 3.40% and 4.27%, respectively. Twelve individuals with cancer died, 7 (21% of the total 33) due to cancer progression, and they had clinical stage IIIA or higher cancer at initial diagnosis. CONCLUSIONS: PWH with both aHSIL and a nadir CD4 cell count ≤200/µL have the highest risk of IAC. PWH who died due to IAC progression had clinical stage IIIA cancer or higher at diagnosis, highlighting the importance of early diagnosis through high-resolution anoscopic screening.

Landscape genomics reveals regions associated with adaptive phenotypic and genetic variation in Ethiopian indigenous chickens.

Climate change is a threat to sustainable livestock production and livelihoods in the tropics. It has adverse impacts on feed and water availability, disease prevalence, production, environmental temperature, and biodiversity. Unravelling the drivers of local adaptation and understanding the underlying genetic variation in random mating indigenous livestock populations informs the design of genetic improvement programmes that aim to increase productivity and resilience. In the present study, we combined environmental, genomic, and phenotypic information of Ethiopian indigenous chickens to investigate their environmental adaptability. Through a hybrid sampling strategy, we captured wide biological and ecological variabilities across the country. Our environmental dataset comprised mean values of 34 climatic, vegetation and soil variables collected over a thirty-year period for 260 geolocations. Our biological dataset included whole genome sequences and quantitative measurements (on eight traits) from 513 individuals, representing 26 chicken populations spread along 4 elevational gradients (6-7 populations per gradient). We performed signatures of selection analyses ([Formula: see text] and XP-EHH) to detect footprints of natural selection, and redundancy analyses (RDA) to determine genotype-environment and genotype-phenotype-associations. RDA identified 1909 outlier SNPs linked with six environmental predictors, which have the highest contributions as ecological drivers of adaptive phenotypic variation. The same method detected 2430 outlier SNPs that are associated with five traits. A large overlap has been observed between signatures of selection identified by[Formula: see text]and XP-EHH showing that both methods target similar selective sweep regions. Average genetic differences measured by [Formula: see text] are low between gradients, but XP-EHH signals are the strongest between agroecologies. Genes in the calcium signalling pathway, those associated with the hypoxia-inducible factor (HIF) transcription factors, and sports performance (GALNTL6) are under selection in high-altitude populations. Our study underscores the relevance of landscape genomics as a powerful interdisciplinary approach to dissect adaptive phenotypic and genetic variation in random mating indigenous livestock populations.

CD8+CD103+PD1+TIM3+ T cells in glioblastoma microenvironment correlate with prognosis.

Glioblastoma, isocitrate dehydrogenase-wildtype (GB), is the most common and aggressive primary brain malignancy with poor outcome. Immune checkpoint inhibitors (ICIs) have been tested in GB and, despite disappointing results, the identification of a small subgroup of responders underlies the need to improve our understanding of the tumour microenvironment (TME) immunity. This study aimed to determine whether the expression of selected immune checkpoints on tissue-resident memory T cells (Trm) may predict patient outcome. We conducted a single cohort observational study. Tumour samples were collected from 45 patients with histologically confirmed GB (WHO grade 4) and processed to obtain single-cell suspensions. Patients were assessed for the correlation of Trm phenotype with overall survival (OS) or progression-free survival (PFS) using multiparametric flow cytometry and uni/multivariate analyses. Levels of Trm expressing programmed cell death protein 1 (PD1) and T cell immunoglobulin and mucin domain-containing protein 3 (TIM3) were found to be linked to clinical outcome. Low frequency of Trm expressing PD1 or TIM3 or both markers defined subgroups as independent positive prognostic factors for patient survival. On multivariate analysis, low CD8+CD103+PD1+TIM3+ Trm and Karnofsky performance status (KPS) ≥70 were confirmed to be the most predictive independent factors associated with longer OS (hazard ratios-HR [95%CI]: 0.14 [0.04-0.52] p < 0.001, 0.39 [0.16-0.96] p = 0.04, respectively). The CD8+CD103+ Trm subgroups were also age-related predictors for survival in GB.

Mpox Epidemiology and Risk Factors, Nigeria, 2022.

To investigate epidemiology of and risk factors for laboratory-confirmed mpox during the 2022 outbreak in Nigeria, we enrolled 265 persons with suspected mpox. A total of 163 (61.5%) were confirmed to have mpox; 137 (84.0%) were adults, 112 (68.7%) male, 143 (87.7%) urban/semi-urban dwellers, 12 (7.4%) self-reported gay men, and 3 (1.8%) female sex workers. Significant risk factors for adults were sexual and nonsexual contact with persons who had mpox, as well as risky sexual behavior. For children, risk factors were close contact with an mpox-positive person and prior animal exposure. Odds of being mpox positive were higher for adults with HIV and lower for those co-infected with varicella zoster virus (VZV). No children were HIV-seropositive; odds of being mpox positive were higher for children with VZV infection. Our findings indicate mpox affects primarily adults in Nigeria, partially driven by sexual activity; childhood cases were driven by close contact, animal exposure, and VZV co-infection.