PySNV for complex intra-host variation detection.

MOTIVATION: Intra-host variants refer to genetic variations or mutations that occur within an individual host organism. These variants are typically studied in the context of viruses, bacteria, or other pathogens to understand the evolution of pathogens. Moreover, intra-host variants are also explored in the field of tumor biology and mitochondrial biology to characterize somatic mutations and inherited heteroplasmic mutations. Intra-host variants can involve long insertions, deletions, and combinations of different mutation types, which poses challenges in their identification. The performance of current methods in detecting of complex intra-host variants is unknown. RESULTS: First, we simulated a dataset comprising 10 samples with 1869 intra-host variants involving various mutation patterns and benchmarked current variant detection software. The results indicated that though current software can detect most variants with F1-scores between 0.76 and 0.97, their performance in detecting long indels and low frequency variants was limited. Thus, we developed a new software, PySNV, for the detection of complex intra-host variations. On the simulated dataset, PySNV successfully detected 1863 variant cases (F1-score: 0.99) and exhibited the highest Pearson correlation coefficient (PCC: 0.99) to the ground truth in predicting variant frequencies. The results demonstrated that PySNV delivered promising performance even for long indels and low frequency variants, while maintaining computational speed comparable to other methods. Finally, we tested its performance on SARS-CoV-2 replicate sequencing data and found that it reported 21% more variants compared to LoFreq, the best-performing benchmarked software, while showing higher consistency (62% over 54%) within replicates. The discrepancies mostly exist in low-depth regions and low frequency variants. AVAILABILITY AND IMPLEMENTATION: https://github.com/bnuLyndon/PySNV/.

Covariate-guided Bayesian mixture of spline experts for the analysis of multivariate high-density longitudinal data.

With rapid development of techniques to measure brain activity and structure, statistical methods for analyzing modern brain-imaging data play an important role in the advancement of science. Imaging data that measure brain function are usually multivariate high-density longitudinal data and are heterogeneous across both imaging sources and subjects, which lead to various statistical and computational challenges. In this article, we propose a group-based method to cluster a collection of multivariate high-density longitudinal data via a Bayesian mixture of smoothing splines. Our method assumes each multivariate high-density longitudinal trajectory is a mixture of multiple components with different mixing weights. Time-independent covariates are assumed to be associated with the mixture components and are incorporated via logistic weights of a mixture-of-experts model. We formulate this approach under a fully Bayesian framework using Gibbs sampling where the number of components is selected based on a deviance information criterion. The proposed method is compared to existing methods via simulation studies and is applied to a study on functional near-infrared spectroscopy, which aims to understand infant emotional reactivity and recovery from stress. The results reveal distinct patterns of brain activity, as well as associations between these patterns and selected covariates.

ACEP: improving antimicrobial peptides recognition through automatic feature fusion and amino acid embedding.

BACKGROUND: Antimicrobial resistance is one of our most serious health threats. Antimicrobial peptides (AMPs), effecter molecules of innate immune system, can defend host organisms against microbes and most have shown a lowered likelihood for bacteria to form resistance compared to many conventional drugs. Thus, AMPs are gaining popularity as better substitute to antibiotics. To aid researchers in novel AMPs discovery, we design computational approaches to screen promising candidates. RESULTS: In this work, we design a deep learning model that can learn amino acid embedding patterns, automatically extract sequence features, and fuse heterogeneous information. Results show that the proposed model outperforms state-of-the-art methods on recognition of AMPs. By visualizing data in some layers of the model, we overcome the black-box nature of deep learning, explain the working mechanism of the model, and find some import motifs in sequences. CONCLUSIONS: ACEP model can capture similarity between amino acids, calculate attention scores for different parts of a peptide sequence in order to spot important parts that significantly contribute to final predictions, and automatically fuse a variety of heterogeneous information or features. For high-throughput AMPs recognition, open source software and datasets are made freely available at https://github.com/Fuhaoyi/ACEP .

Immune evasion and ACE2 binding affinity contribute to SARS-CoV-2 evolution.

Mutations in the SARS-CoV-2 genome could confer resistance to pre-existing antibodies and/or increased transmissibility. The recently emerged Omicron subvariants exhibit a strong tendency for immune evasion, suggesting adaptive evolution. However, because previous studies have been limited to specific lineages or subsets of mutations, the overall evolutionary trajectory of SARS-CoV-2 and the underlying driving forces are still not fully understood. Here we analysed all open-access SARS-CoV-2 genomes (up to November 2022) and correlated the mutation incidence and fitness changes with the impacts of mutations on immune evasion and ACE2 binding affinity. Our results show that the Omicron lineage had an accelerated mutation rate in the RBD region, while the mutation incidence in other genomic regions did not change dramatically over time. Mutations in the RBD region exhibited a lineage-specific pattern and tended to become more aggregated over time, and the mutation incidence was positively correlated with the strength of antibody pressure. Additionally, mutation incidence was positively correlated with changes in ACE2 binding affinity, but with a lower correlation coefficient than with immune evasion. In contrast, the effect of mutations on fitness was more closely correlated with changes in ACE2 binding affinity than with immune evasion. Our findings suggest that immune evasion and ACE2 binding affinity play significant and diverse roles in the evolution of SARS-CoV-2.

A longitudinal epigenome-wide association study of preeclamptic and normotensive pregnancy.

Background: While preeclampsia (PE) is a leading cause of pregnancy-related morbidity/mortality, its underlying mechanisms are not fully understood. DNA methylation (DNAm) is a dynamic regulator of gene expression that may offer insight into PE pathophysiology and/or serve as a biomarker (e.g., risk, subtype, a therapeutic response). This study's purpose was to evaluate for differences in blood-based DNAm across all trimesters between individuals eventually diagnosed with PE (cases) and individuals who remained normotensive throughout pregnancy, did not develop proteinuria, and birthed a normally grown infant (controls). Results: In the discovery phase, longitudinal, genome-wide DNAm data were generated across three trimesters of pregnancy in 56 participants (n=28 cases, n=28 controls) individually matched on self-identified race, pre-pregnancy body mass index, smoking, and gestational age at sample collection. An epigenome-wide association study (EWAS) was conducted, using surrogate variable analysis to account for unwanted sources of variation. No CpGs met the genome-wide significance p value threshold of 9×10-8, but 16 CpGs (trimester 1: 5; trimester 2: 1; trimester 3: 10) met the suggestive significance threshold of 1×10-5. DNAm data were also evaluated for differentially methylated regions (DMRs) by PE status. Three DMRs in each trimester were significant after Bonferonni-adjustment. Since only third-trimester samples were available from an independent replication sample (n=64 cases, n=50 controls), the top suggestive hits from trimester 3 (cg16155413 and cg21882990 associated with TRAF3IP2-AS1/TRAF3IP2 genes, which also made up the top DMR) were carried forward for replication. During replication, DNAm data were also generated for validation purposes from discovery phase third trimester samples. While significant associations between DNAm and PE status were observed at both sites in the validation sample, no associations between DNAm and PE status were observed in the independent replication sample. Conclusions: The discovery phase findings for cg16155413/cg21882990 (TRAF3IP2-AS1/TRAF3IP2) were validated with a new platform but were not replicated in an independent sample. Given the differences in participant characteristics between the discovery and replication samples, we cannot rule out important signals for these CpGs. Additional research is warranted for cg16155413/cg21882990, as well as top hits in trimesters 1-2 and significant DMRs that were not examined in the replication phase.

Preconception stress exposure from childhood to adolescence and birth outcomes: The impact of stress type, severity and consistency.

The negative effects of prenatal stress on offspring health are well established, but there remains little understanding of the influence of stress prior to conception despite known effects on biological systems that are important for a healthy pregnancy. Furthermore, operational definitions of stress vary considerably, and exposure is often characterized via summed, ordinal scales of events. We hypothesized that type, severity, and consistency of preconception stress would be associated with birthweight and gestational age (GA) at birth. Data were drawn from a subsample of participants in the 21-year longitudinal Pittsburgh Girls Study (PGS, N = 2,450) that has followed women annually since childhood. Prior work in the PGS derived three domains of stress exposure between ages 7-17 years related to subsistence (e.g., resource strain, overcrowding), safety (e.g., community violence, inter-adult aggression), and caregiving (e.g., separation, maternal depression). We tested the effects of dimensions of preconception stress on birthweight and GA among offspring of 490 PGS participants who delivered at age 18 or older (n = 490; 76% Black, 20% White, 4% Multiracial). Our hypotheses were partially supported with results varying by stress type and severity and by infant sex. Severity of preconception exposure to subsistence stress was prospectively associated with lower offspring birthweight (B = -146.94, SE = 69.07, 95% CI = -282.66, -11.22). The association between severity of caregiving stress in childhood and adolescence and GA at birth was moderated by infant sex (B = 0.85, SE = .41, 95% CI = 0.04, 1.66), suggesting greater vulnerability to this type of stress for male compared to female infants. Exposure to safety stressors did not predict birth outcomes. Infants of Black compared with White mothers had lower birthweight in all models regardless of preconception stress type, severity or consistency. However, we observed no moderating effects of race on preconception stress-birth outcome associations. Demonstrating specificity of associations between preconception stress exposure and prenatal health has the potential to inform preventive interventions targeting profiles of exposure to optimize birth outcomes.

Serum α-KL, a potential early marker of diabetes complications in youth with T1D, is regulated by miRNA 192.

Despite the wealth of information on biomarkers of diabetes complications in adults with type 1 diabetes, data in the pediatric population is limited. Diabetic nephropathy (DN), the leading cause of mortality in type 1 diabetes T1D), could be potentially missed in youth, as albuminuria, the current "gold" standard, may be transient and may not reflect permanent renal impairment. Soluble alpha KL has emerged as a potential marker of early diabetic nephropathy. Seventy-nine pediatric patients with type 1 diabetes meeting ISPAD criteria for nephropathy screening were consecutively recruited (90% Caucasian, 51% male, mean age 16.1 ± 3.1 years, duration of T1D 7.2 ± 3.9 years, 2-year average HbA1c 8.0 ± 1.3%, and serum and urine samples were collected for analysis. Serum Klotho (KL) and circulating miRNA levels of select miRNA involved in the pathogenesis of DN were estimated. KL had a strong inverse correlation with diabetes duration and HbA1c, two important risk factors in the development of diabetes complications. Serum miR-192 were negatively associated with KL among children with prolonged duration of diabetes (≥12 years) after adjustment for age and sex. In cell culture, overexpression of miR-192 significantly downregulated KL mRNA and protein levels, and reduced KL levels in the media. miR-192 mimic reduced luciferase activity in a reporter containing the KL 3' UTR (60% compared to controls, p<0.01), and the inhibitor rescued it. Deletion of a potential binding site for miR-192 in the KL 3'UTR completely abolished the effect of miR-192 in the reporter assay, suggesting that KL is a direct target gene of miR-192. Overexpression of miR-192 significantly increased oxidative stress (MDA) and expression of inflammatory and senescence markers IL-6 and p16. Inhibition of miR-192 significantly reduced levels of MDA, IL-6 and p16. In summary, we demonstrate an increase in miR-192 and a decrease in KL levels in children with prolonged duration of T1D. We demonstrate a novel role for miR-192 in directly regulating KL levels, and through that, senescence and oxidative stress, key pathological processes in the development of DN. miR-192 and/or KL levels are altered with severity and duration of diabetes and could serve as early biomarkers for DN.

The missense variant, rs373863828, in CREBRF plays a role in longitudinal changes in body mass index in Samoans.

OBJECTIVE: A missense variant, rs373863828, in CREBRF is associated with obesity in Polynesians. We investigate whether rs373863828 and other factors are associated with body mass index (BMI) rate-of-change between 2010 and 2017-19 in Samoans. METHODS: We used sex-stratified models to test whether BMI rate-of-change was associated with rs373863828, baseline BMI, age, residence, physical activity, and household asset score in a cohort study of 480 Samoan adults measured in both 2010 (mean age 43.8 years) and 2017-19. RESULTS: Mean BMI increased from 32.1 to 33.5 kg/m2 in males (n = 220, p = 1.3 ×10-8) and from 35.9 to 37.8 kg/m2 in females (n = 260, p = 1.2 ×10-13). In females, the A allele was associated with a higher rate-of-change (0.150 kg/m2/year/allele, p = 1.7 ×10-4). Across 10-year age groups, mean BMI rate-of-change was lower in older participants. The BMI rate of change differed by genotype: it was, in females with AA genotype, approximately half that seen in GG and AG participants. In females lower baseline household asset scores were associated with a higher rate-of-change (p = 0.002). CONCLUSIONS: In Samoans, the minor A allele of rs373863828 is associated with an increased rate-of-change in BMI in females. On average, BMI of females with the AA genotype increased 0.30 kg/m2/year more than of those with the GG genotype.

Genomic Perspectives on the Emerging SARS-CoV-2 Omicron Variant.

A new variant of concern for SARS-CoV-2, Omicron (B.1.1.529), was designated by the World Health Organization on November 26, 2021. This study analyzed the viral genome sequencing data of 108 samples collected from patients infected with Omicron. First, we found that the enrichment efficiency of viral nucleic acids was reduced due to mutations in the region where the primers anneal to. Second, the Omicron variant possesses an excessive number of mutations compared to other variants circulating at the same time (median: 62 vs. 45), especially in the Spike gene. Mutations in the Spike gene confer alterations in 32 amino acid residues, more than those observed in other SARS-CoV-2 variants. Moreover, a large number of nonsynonymous mutations occur in the codons for the amino acid residues located on the surface of the Spike protein, which could potentially affect the replication, infectivity, and antigenicity of SARS-CoV-2. Third, there are 53 mutations between the Omicron variant and its closest sequences available in public databases. Many of these mutations were rarely observed in public databases and had a low mutation rate. In addition, the linkage disequilibrium between these mutations was low, with a limited number of mutations concurrently observed in the same genome, suggesting that the Omicron variant would be in a different evolutionary branch from the currently prevalent variants. To improve our ability to detect and track the source of new variants rapidly, it is imperative to further strengthen genomic surveillance and data sharing globally in a timely manner.

Capturing the dynamic nature of stress exposure in the Pittsburgh Girls Study.

BACKGROUND: The science of stress exposure and health in humans has been hampered by differences in operational definitions of exposures and approaches to defining timing, leading to results that lack consistency and specificity. In the present study we aim to empirically derive variability in type, timing and chronicity of stress exposure for Black and White females using prospectively collected data in the Pittsburgh Girls Study (PGS). METHODS: The PGS is an ongoing 20-year longitudinal, community-based study. In this paper we focused on annual caregiver reports of three domains of stress: subsistence (e.g., resource strain, overcrowding); safety (e.g., community violence, inter-adult aggression), and caregiving (e.g., separation, maternal depression) from early childhood through adolescence. Z-scores were used to conduct a finite mixture model-based latent class trajectory analysis. Model fit was compared using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). We examined differences in timing and chronicity of stress exposure between Black and White girls. RESULTS: Distinct trajectory groups characterized by differential timing and chronicity of stress exposure were observed across all stress domains. Six trajectories characterized subsistence and safety stress, and five characterized caregiving stress. Variability in initial level, chronicity, and magnitude and timing of change was observed within and across domains of stressors. Race differences also varied across the domains: race differences in timing and chronicity were most pronounced for the subsistence and safety domains, whereas Black and White girls had similar levels of exposure to caregiving stress. CONCLUSIONS: Substantial variability in timing and chronicity was observed within and across stress domains. Modeling specific domains and dimensions of stress exposure is likely important in testing associations between exposure and health; such specificity may lead to more effective deployment of preventive interventions based on stress exposure.

Measuring Variability in Rest-Activity Rhythms from Actigraphy with Application to Characterizing Symptoms of Depression.

The twenty-four hour sleep-wake pattern known as the rest-activity rhythm (RAR) is associated with many aspects of health and well-being. Researchers have utilized a number of interpretable, person-specific RAR measures that can be estimated from actigraphy. Actigraphs are wearable devices that dynamically record acceleration and provide indirect measures of physical activity over time. One class of useful RAR measures are those that quantify variability around a mean circadian pattern. However, current parametric and nonparametric RAR measures used by applied researchers can only quantify variability from a limited or undefined number of rhythmic sources. The primary goal of this article is to consider a new measure of RAR variability: the log-power spectrum of stochastic error around a circadian mean. This functional measure quantifies the relative contributions of variability about a circadian mean from all possibly frequencies, including weekly, daily, and high-frequency sources of variation. It can be estimated through a two-stage procedure that smooths the log-periodogram of residuals after estimating a circadian mean. The development of this measure was motivated by a study of depression in older adults and revealed that slow, rhythmic variations in activity from a circadian pattern are correlated with depression symptoms.

[Isolation of filamentous fungi capable of enhancing sludge dewaterability and study of mechanisms responsible for the sludge dewaterability enhancement].

To study the influence of filamentous fungi on the sludge dewaterability is very significant for the development of biological treatment methods for enhancing sludge dewaterability. In this study, filamentous fungi capable of enhancing sludge dewaterability were isolated from sewage sludge and the related mechanisms responsible for the sludge dewaterability enhancement were investigated. A filamentous fungus Mucor circinelloides ZG-3 was successfully isolated from sludge, and sludge dewaterability could be drastically improved by this fungus. Further study revealed that the enhancement of sludge dewaterability was influenced by inoculation method, inoculum size and solid content of sludge. The optimal inoculation method was mycelia inoculation, the optimal inoculum size was 10%, and the optimal solid content of sludge was about 4%. Under the optimized conditions, the specific resistance to filtration (SRF) of sludge could be decreased by 75.1% after being treated by M. circinelloides ZG-3. After the treatment, the COD value of sludge supernatant was only 310 mg x L(-1), and the treated sludge still exhibited good settleability. During the treatment of sewage sludge by M. circinelloides ZG-3, the mechanisms responsible for the sludge dewaterability enhancement included the degradation of sludge extracellular polymeric substances (EPS) and the decrease of sludge pH. Therefore, the treatment of sewage sludge using M. circinelloides ZG-3 is a useful and novel method for sludge conditioning.