A model-based hierarchical Bayesian approach to Sholl analysis.

MOTIVATION: Due to the link between microglial morphology and function, morphological changes in microglia are frequently used to identify pathological immune responses in the central nervous system. In the absence of pathology, microglia are responsible for maintaining homeostasis, and their morphology can be indicative of how the healthy brain behaves in the presence of external stimuli and genetic differences. Despite recent interest in high throughput methods for morphological analysis, Sholl analysis is still widely used for quantifying microglia morphology via imaging data. Often, the raw data are naturally hierarchical, minimally including many cells per image and many images per animal. However, existing methods for performing downstream inference on Sholl data rely on truncating this hierarchy so rudimentary statistical testing procedures can be used. RESULTS: To fill this longstanding gap, we introduce a parametric hierarchical Bayesian model-based approach for analyzing Sholl data, so that inference can be performed without aggressive reduction of otherwise very rich data. We apply our model to real data and perform simulation studies comparing the proposed method with a popular alternative. AVAILABILITY AND IMPLEMENTATION: Software to reproduce the results presented in this article is available at: https://github.com/vonkaenelerik/hierarchical_sholl. An R package implementing the proposed models is available at: https://github.com/vonkaenelerik/ShollBayes.

Patterns of Unwanted Biological and Technical Expression Variation Among 49 Human Tissues.

Tissue gene expression studies are impacted by biological and technical sources of variation, which can be broadly classified into wanted and unwanted variation. The latter, if not addressed, results in misleading biological conclusions. Methods have been proposed to reduce unwanted variation, such as normalization and batch correction. A more accurate understanding of all causes of variation could significantly improve the ability of these methods to remove unwanted variation while retaining variation corresponding to the biological question of interest. We used 17,282 samples from 49 human tissues in the Genotype-Tissue Expression data set (v8) to investigate patterns and causes of expression variation. Transcript expression was transformed to z-scores, and only the most variable 2% of transcripts were evaluated and clustered based on coexpression patterns. Clustered gene sets were assigned to different biological or technical causes based on histologic appearances and metadata elements. We identified 522 variable transcript clusters (median: 11 per tissue) among the samples. Of these, 63% were confidently explained, 16% were likely explained, 7% were low confidence explanations, and 14% had no clear cause. Histologic analysis annotated 46 clusters. Other common causes of variability included sex, sequencing contamination, immunoglobulin diversity, and compositional tissue differences. Less common biological causes included death interval (Hardy score), disease status, and age. Technical causes included blood draw timing and harvesting differences. Many of the causes of variation in bulk tissue expression were identifiable in the Tabula Sapiens data set of single-cell expression. This is among the largest explorations of the underlying sources of tissue expression variation. It uncovered expected and unexpected causes of variable gene expression and demonstrated the utility of matched histologic specimens. It further demonstrated the value of acquiring meaningful tissue harvesting metadata elements to use for improved normalization, batch correction, and analysis of both bulk and single-cell RNA-seq data.

Fast and fierce versus slow and smooth: Heterogeneity in immune responses to Plasmodium in the controlled human malaria infection model.

Controlled human malaria infection (CHMI) is an established model in clinical malaria research. Upon exposure to Plasmodium falciparum parasites, malaria-naive volunteers differ in dynamics and composition of their immune profiles and subsequent capacity to generate protective immunity. CHMI volunteers are either inflammatory responders who have prominent cellular IFN-γ production primarily driven by adaptive T cells, or tempered responders who skew toward antibody-mediated humoral immunity. When exposed to consecutive CHMIs under antimalarial chemoprophylaxis, individuals who can control parasitemia after a single immunization (fast responders) are more likely to be protected against a subsequent challenge infection. Fast responders tend to be inflammatory responders who can rapidly induce long-lived IFN-γ+ T cell responses. Slow responders or even non-responders can also be protected, but via a more diverse range of responses that take a longer time to reach full protective efficacy, in part due to their tempered phenotype. The latter group can be identified at baseline before CHMI by higher expression of inhibitory ligands CTLA-4 and TIM-3 on CD4+ T cells. Delineating heterogeneity in human immune responses to P. falciparum will facilitate rational design and strategy towards effective malaria vaccines.

Modeling the Impact of a Highly Potent Plasmodium falciparum Transmission-Blocking Monoclonal Antibody in Areas of Seasonal Malaria Transmission.

Transmission-blocking interventions can play an important role in combating malaria worldwide. Recently, a highly potent Plasmodium falciparum transmission-blocking monoclonal antibody (TB31F) was demonstrated to be safe and efficacious in malaria-naive volunteers. Here we predict the potential public health impact of large-scale implementation of TB31F alongside existing interventions. We developed a pharmaco-epidemiological model, tailored to 2 settings of differing transmission intensity with already established insecticide-treated nets and seasonal malaria chemoprevention interventions. Community-wide annual administration (at 80% coverage) of TB31F over a 3-year period was predicted to reduce clinical incidence by 54% (381 cases averted per 1000 people per year) in a high-transmission seasonal setting, and 74% (157 cases averted per 1000 people per year) in a low-transmission seasonal setting. Targeting school-aged children gave the largest reduction in terms of cases averted per dose. An annual administration of the transmission-blocking monoclonal antibody TB31F may be an effective intervention against malaria in seasonal malaria settings.

Whole sporozoite immunization with Plasmodium falciparum strain NF135 in a randomized trial.

BACKGROUND: Whole sporozoite immunization under chemoprophylaxis (CPS regime) induces long-lasting sterile homologous protection in the controlled human malaria infection model using Plasmodium falciparum strain NF54. The relative proficiency of liver-stage parasite development may be an important factor determining immunization efficacy. Previous studies show that Plasmodium falciparum strain NF135 produces relatively high numbers of large liver-stage schizonts in vitro. Here, we evaluate this strain for use in CPS immunization regimes. METHODS: In a partially randomized, open-label study conducted at the Radboudumc, Nijmegen, the Netherlands, healthy, malaria-naïve adults were immunized by three rounds of fifteen or five NF135-infected mosquito bites under mefloquine prophylaxis (cohort A) or fifteen NF135-infected mosquito bites and presumptive treatment with artemether/lumefantrine (cohort B). Cohort A participants were exposed to a homologous challenge 19 weeks after immunization. The primary objective of the study was to evaluate the safety and tolerability of CPS immunizations with NF135. RESULTS: Relatively high liver-to-blood inocula were observed during immunization with NF135 in both cohorts. Eighteen of 30 (60%) high-dose participants and 3/10 (30%) low-dose participants experienced grade 3 adverse events 7 to 21 days following their first immunization. All cohort A participants and two participants in cohort B developed breakthrough blood-stage malaria infections during immunizations requiring rescue treatment. The resulting compromised immunizations induced modest sterile protection against homologous challenge in cohort A (5/17; 29%). CONCLUSIONS: These CPS regimes using NF135 were relatively poorly tolerated and frequently required rescue treatment, thereby compromising immunization efficiency and protective efficacy. Consequently, the full potential of NF135 sporozoites for induction of immune protection remains inconclusive. Nonetheless, the high liver-stage burden achieved by this strain highlights it as an interesting potential candidate for novel whole sporozoite immunization approaches. TRIAL REGISTRATION: The trial was registered at ClinicalTrials.gov under identifier NCT03813108.

The effect of tissue composition on gene co-expression.

Variable cellular composition of tissue samples represents a significant challenge for the interpretation of genomic profiling studies. Substantial effort has been devoted to modeling and adjusting for compositional differences when estimating differential expression between sample types. However, relatively little attention has been given to the effect of tissue composition on co-expression estimates. In this study, we illustrate the effect of variable cell-type composition on correlation-based network estimation and provide a mathematical decomposition of the tissue-level correlation. We show that a class of deconvolution methods developed to separate tumor and stromal signatures can be applied to two component cell-type mixtures. In simulated and real data, we identify conditions in which a deconvolution approach would be beneficial. Our results suggest that uncorrelated cell-type-specific markers are ideally suited to deconvolute both the expression and co-expression patterns of an individual cell type. We provide a Shiny application for users to interactively explore the effect of cell-type composition on correlation-based co-expression estimation for any cell types of interest.

Low SARS-CoV-2 Cq values in healthcare workers with symptomatic COVID-19 infections, regardless of symptom severity, The Netherlands, January to August 2022.

We analysed SARS-CoV-2 PCR Cq values from 3,183 healthcare workers who tested positive between January and August 2022. Median Cq values were lower in symptomatic than in asymptomatic HCW. The difference in Cq values between HCW with mild vs moderate/severe symptoms was statistically significant but negligibly small. To prevent nosocomial infections, all symptomatic HCW should be tested irrespective of symptom severity. This information can support decisions on testing and isolation, in the context of ongoing pressure on healthcare systems.

Autoregressive modeling and diagnostics for qPCR amplification.

MOTIVATION: Current methods used to analyze real-time quantitative polymerase chain reaction (qPCR) data exhibit systematic deviations from the assumed model over the progression of the reaction. Slight variations in the amount of the initial target molecule or in early amplifications are likely responsible for these deviations. Commonly used 4- and 5-parameter sigmoidal models appear to be particularly susceptible to this issue, often displaying patterns of autocorrelation in the residuals. The presence of this phenomenon, even for technical replicates, suggests that these parametric models may be misspecified. Specifically, they do not account for the sequential dependent nature of the amplification process that underlies qPCR fluorescence measurements. RESULTS: We demonstrate that a Smooth Transition Autoregressive (STAR) model addresses this limitation by explicitly modeling the dependence between cycles and the gradual transition between amplification regimes. In summary, application of a STAR model to qPCR amplification data improves model fit and reduces autocorrelation in the residuals. AVAILABILITY AND IMPLEMENTATION: R scripts to reproduce all the analyses and results described in this manuscript can be found at: https://github.com/bhsu4/GAPDH.SO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Non-tuberculous mycobacteria isolation from presumptive tuberculosis patients in Lambaréné, Gabon.

OBJECTIVE: The prevalence of clinical cases of pulmonary non-tuberculous mycobacteria (NTM) is increasing worldwide. The aim of this study was to determine the proportion and the NTM species isolated from presumptive tuberculosis patients in Lambaréné, Gabon. METHOD: From January 2018 to December 2020, sputum samples from presumptive TB patients were analysed at the tuberculosis reference laboratory of the Centre de Recherches Médicales de Lambaréné. Two sputum samples were collected per patient, and culture was performed using Bactec MGIT 960. The GenoType Mycobacterium CM/AS was used for NTM isolates confirmation and species differentiation. RESULTS: Among 1363 sputum samples analysed, 285 (20.9%) were Auramin acid fast bacilli (AFB) smear-positive. NTM were isolated in 137/1363 (10%) of the samples. The most prevalent NTM species was Mycobacterium intracellulare (n = 74; 54%). CONCLUSION: These results show the presence of NTM among presumptive TB patients in Gabon, which could potentially complicate TB diagnosis. This presents a new public health challenge, and emphasises the need to consider NTM in planning the prevention and management of tuberculosis control.

A systems genomics approach uncovers molecular associates of RSV severity.

Respiratory syncytial virus (RSV) infection results in millions of hospitalizations and thousands of deaths each year. Variations in the adaptive and innate immune response appear to be associated with RSV severity. To investigate the host response to RSV infection in infants, we performed a systems-level study of RSV pathophysiology, incorporating high-throughput measurements of the peripheral innate and adaptive immune systems and the airway epithelium and microbiota. We implemented a novel multi-omic data integration method based on multilayered principal component analysis, penalized regression, and feature weight back-propagation, which enabled us to identify cellular pathways associated with RSV severity. In both airway and immune cells, we found an association between RSV severity and activation of pathways controlling Th17 and acute phase response signaling, as well as inhibition of B cell receptor signaling. Dysregulation of both the humoral and mucosal response to RSV may play a critical role in determining illness severity.

Correlating efficacy and immunogenicity in malaria vaccine trials.

The availability of an effective and appropriately implemented malaria vaccine would form a crucial cornerstone of public health efforts to fight this disease. Despite many decades of research, however, no malaria vaccine has yet shown satisfactory protective efficacy or been rolled-out. Validated immunological substitute endpoints have the potential to accelerate clinical vaccine development by reducing the required complexity, size, duration and cost of clinical trials. Besides facilitating clinical development of existing vaccine candidates, understanding immunological mechanisms of protection may drive the development of fundamentally new vaccination approaches. In this review we focus on correlates of protection in malaria vaccine development: Does immunogenicity predict malaria vaccine efficacy and why is this question particularly difficult? Have immunological correlates accelerated malaria vaccine development in the past and will they facilitate it in the future? Does Controlled Human Malaria Infection represent a valid model for identifying such immunological correlates, or a correlate of protection against naturally-acquired malaria in itself?

Airway Gene Expression Correlates of Respiratory Syncytial Virus Disease Severity and Microbiome Composition in Infants.

BACKGROUND: Respiratory syncytial virus (RSV) is the leading cause of severe respiratory disease in infants. The causes and correlates of severe illness in the majority of infants are poorly defined. METHODS: We recruited a cohort of RSV-infected infants and simultaneously assayed the molecular status of their airways and the presence of airway microbiota. We used rigorous statistical approaches to identify gene expression patterns associated with disease severity and microbiota composition, separately and in combination. RESULTS: We measured comprehensive airway gene expression patterns in 106 infants with primary RSV infection. We identified an airway gene expression signature of severe illness dominated by excessive chemokine expression. We also found an association between Haemophilus influenzae, disease severity, and airway lymphocyte accumulation. Exploring the time of onset of clinical symptoms revealed acute activation of interferon signaling following RSV infection in infants with mild or moderate illness, which was absent in subjects with severe illness. CONCLUSIONS: Our data reveal that airway gene expression patterns distinguish mild/moderate from severe illness. Furthermore, our data identify biomarkers that may be therapeutic targets or useful for measuring efficacy of intervention responses.

Spatial Proteomic Approach to Characterize Skeletal Muscle Myofibers.

Skeletal muscle myofibers have differential protein expression resulting in functionally distinct slow- and fast-twitch types. While certain protein classes are well-characterized, the depth of all proteins involved in this process is unknown. We utilized the Human Protein Atlas (HPA) and the HPASubC tool to classify mosaic expression patterns of staining across 49,600 unique tissue microarray (TMA) images using a visual proteomic approach. We identified 2164 proteins with potential mosaic expression, of which 1605 were categorized as "likely" or "real." This list included both well-known fiber-type-specific and novel proteins. A comparison of the 1605 mosaic proteins with a mass spectrometry (MS)-derived proteomic dataset of single human muscle fibers led to the assignment of 111 proteins to fiber types. We additionally used a multiplexed immunohistochemistry approach, a multiplexed RNA-ISH approach, and STRING v11 to further assign or suggest fiber types of newly characterized mosaic proteins. This visual proteomic analysis of mature skeletal muscle myofibers greatly expands the known repertoire of twitch-type-specific proteins.

Identification of the novel Ido1 imprinted locus and its potential epigenetic role in pregnancy loss.

Previous studies show that aberrant tryptophan catabolism reduces maternal immune tolerance and adversely impacts pregnancy outcomes. Tryptophan depletion in pregnancy is facilitated by increased activity of tryptophan-depleting enzymes [i.e. the indolamine-2,3 dioxygenase (IDO)1 and IDO2) in the placenta. In mice, inhibition of IDO1 activity during pregnancy results in fetal loss; however, despite its important role, regulation of Ido1 gene transcription is unknown. The current study shows that the Ido1 and Ido2 genes are imprinted and maternally expressed in mouse placentas. DNA methylation analysis demonstrates that nine CpG sites at the Ido1 promoter constitute a differentially methylated region that is highly methylated in sperm but unmethylated in oocytes. Bisulfite cloning sequencing analysis shows that the paternal allele is hypermethylated while the maternal allele shows low levels of methylation in E9.5 placenta. Further study in E9.5 placentas from the CBA/J X DBA/2 spontaneous abortion mouse model reveals that aberrant methylation of Ido1 is linked to pregnancy loss. DNA methylation analysis in humans shows that IDO1 is hypermethylated in human sperm but partially methylated in placentas, suggesting similar methylation patterns to mouse. Importantly, analysis in euploid placentas from first trimester pregnancy loss reveals that IDO1 methylation significantly differs between the two placenta cohorts, with most CpG sites showing increased percent of methylation in miscarriage placentas. Our study suggests that DNA methylation is linked to regulation of Ido1/IDO1 expression and altered Ido1/IDO1 DNA methylation can adversely influence pregnancy outcomes.

Big Strides in Cellular MicroRNA Expression.

A lack of knowledge of the cellular origin of miRNAs has greatly confounded functional and biomarkers studies. Recently, three studies characterized miRNA expression patterns across >78 human cell types. These combined data expand our knowledge of miRNA expression localization and confirm that many miRNAs show cell type-specific expression patterns.

Association of low birth weight and polyparasitic infection during pregnancy in Lambaréné, Gabon.

OBJECTIVE: To report the prevalence of polyparasitism during pregnancy in the Lambaréné region of Gabon and its association with newborn birth weight. METHOD: Pregnant women in their third trimester were recruited in a prospective study between November 2011 and March 2015. Parasite infection status was assessed microscopically in stool, urine and blood samples. Maternal demographic and obstetrical characteristics and newborns anthropometric data were collected. Multivariable logistic regression was used to assess the association between low birth weight and polyparasitism. RESULTS: 678 of 927 pregnant women were included for analysis with mean age (SD) of 25 (6.8) years. The analysis showed that 69% (468/678) were infected with at least one parasite (Plasmodium spp., Schistosoma spp., soil-transmitted helminths, filarial infections). This comprised of 38% with monoparasitism and 31% polyparasitism. The proportion of newborn babies with a weight below 2500 g (LBW) in our study was 21% (142/678). Compared to pregnant women without infection, women with monoparasitic infection had adjusted Odds Ratio confidence interval 95% CI (aOR [95%CI]) of 1.6 [0.95-2.73], those with two parasites had aOR 95%CI of 2.63 [1.51-4.62], and those with more than two parasites had aOR of 5.08 [2.5-10.38] for delivering a newborn with low birth weight. CONCLUSION: In Lambaréné, an endemic area for multiple parasite infections, there is a high prevalence of polyparasitism in pregnant women. Polyparasitism is associated with low birth weight. Therefore, there is an urgent need for active screening and treatment of parasite infections in pregnant women to assess the potential public health benefit of such interventions.

Smarthealth technology study protocol to improve relationships between older adults with dementia and family caregivers.

AIM: The aim of this study is to develop a Smarthealth system of monitoring, modelling, and interactive recommendation solutions (for caregivers) for in-home dementia patient care that focuses on caregiver-patient relationships. DESIGN: This descriptive study employs a single-group, non-randomized trial to examine functionality, effectiveness, feasibility, and acceptability of the novel Smarthealth system. METHODS: Thirty persons with Alzheimer's Disease or related dementia and their family caregivers (N = 30 dyads) will receive and install Smarthealth technology in their home. There will be a 1-month observation phase for collecting baseline mood states and a 2-month implementation phase when caregivers will receive stress management techniques for each detected, negative mood state. Caregivers will report technique implementation and usefulness, sent via Ecological Momentary Assessment system to the study-provided smartphone. Caregivers will provide daily, self-reported mood and health ratings. Instruments measuring caregiver assessment of disruptive behaviours and their effect on caregivers; caregiver depressive symptoms, anxiety and stress; caregiver strain; and family functioning will be completed at baseline and 3 months. The study received funding in 2018 and ethics board approval in 2019. DISCUSSION: This study will develop and test novel in-home technology to improve family caregiving relationships. Results from this study will help develop and improve the Smarthealth recommendation system and determine its usefulness, feasibility, and acceptability for persons with dementia and their family caregiver. IMPACT: The Smarthealth technology discussed will provide in-home stress reduction resources at a time when older adults may be experiencing increasingly high rates of isolation and anxiety and caregiver dyads may be experiencing high levels of relationship strain. TRIAL REGISTRATION: This study was registered with Clinical Trials.gov (Identifier NCT04536701).

Multiple imputation and direct estimation for qPCR data with non-detects.

BACKGROUND: Quantitative real-time PCR (qPCR) is one of the most widely used methods to measure gene expression. An important aspect of qPCR data that has been largely ignored is the presence of non-detects: reactions failing to exceed the quantification threshold and therefore lacking a measurement of expression. While most current software replaces these non-detects with a value representing the limit of detection, this introduces substantial bias in the estimation of both absolute and differential expression. Single imputation procedures, while an improvement on previously used methods, underestimate residual variance, which can lead to anti-conservative inference. RESULTS: We propose to treat non-detects as non-random missing data, model the missing data mechanism, and use this model to impute missing values or obtain direct estimates of model parameters. To account for the uncertainty inherent in the imputation, we propose a multiple imputation procedure, which provides a set of plausible values for each non-detect. We assess the proposed methods via simulation studies and demonstrate the applicability of these methods to three experimental data sets. We compare our methods to mean imputation, single imputation, and a penalized EM algorithm incorporating non-random missingness (PEMM). The developed methods are implemented in the R/Bioconductor package nondetects. CONCLUSIONS: The statistical methods introduced here reduce discrepancies in gene expression values derived from qPCR experiments in the presence of non-detects, providing increased confidence in downstream analyses.

Toward the human cellular microRNAome.

MicroRNAs are short RNAs that serve as regulators of gene expression and are essential components of normal development as well as modulators of disease. MicroRNAs generally act cell-autonomously, and thus their localization to specific cell types is needed to guide our understanding of microRNA activity. Current tissue-level data have caused considerable confusion, and comprehensive cell-level data do not yet exist. Here, we establish the landscape of human cell-specific microRNA expression. This project evaluated 8 billion small RNA-seq reads from 46 primary cell types, 42 cancer or immortalized cell lines, and 26 tissues. It identified both specific and ubiquitous patterns of expression that strongly correlate with adjacent superenhancer activity. Analysis of unaligned RNA reads uncovered 207 unknown minor strand (passenger) microRNAs of known microRNA loci and 495 novel putative microRNA loci. Although cancer cell lines generally recapitulated the expression patterns of matched primary cells, their isomiR sequence families exhibited increased disorder, suggesting DROSHA- and DICER1-dependent microRNA processing variability. Cell-specific patterns of microRNA expression were used to de-convolute variable cellular composition of colon and adipose tissue samples, highlighting one use of these cell-specific microRNA expression data. Characterization of cellular microRNA expression across a wide variety of cell types provides a new understanding of this critical regulatory RNA species.

Recombinase Polymerase Amplification and Lateral Flow Assay for Ultrasensitive Detection of Low-Density Plasmodium falciparum Infection from Controlled Human Malaria Infection Studies and Naturally Acquired Infections.

Microscopy and rapid diagnostic tests (RDTs) are the main diagnostic tools for malaria but fail to detect low-density parasitemias that are important for maintaining malaria transmission. To complement existing diagnostic methods, an isothermal reverse transcription-recombinase polymerase amplification and lateral flow assay (RT-RPA) was developed. We compared the performance with that of ultrasensitive reverse transcription-quantitative PCR (uRT-qPCR) using nucleic acid extracts from blood samples (n = 114) obtained after standardized controlled human malaria infection (CHMI) with Plasmodium falciparum sporozoites. As a preliminary investigation, we also sampled asymptomatic individuals (n = 28) in an area of malaria endemicity (Lambaréné, Gabon) to validate RT-RPA and assess its performance with unprocessed blood samples (dbRT-RPA). In 114 samples analyzed from CHMI trials, the positive percent agreement to uRT-qPCR was 90% (95% confidence interval [CI], 80 to 96). The negative percent agreement was 100% (95% CI, 92 to 100). The lower limit of detection was 64 parasites/ml. In Gabon, RT-RPA was 100% accurate with asymptomatic volunteers (n = 28), while simplified dbRT-RPA showed 89% accuracy. In a subgroup analysis, RT-RPA detected 9/10 RT-qPCR-positive samples, while loop-mediated isothermal amplification (LAMP) detected 2/10. RT-RPA is a reliable diagnostic test for asymptomatic low-density infections. It is particularly useful in settings where uRT-qPCR is difficult to implement.