Over the past thirty years, the importance of chemokines and their seven-transmembrane G protein-coupled receptors (GPCRs) has been increasingly recognized. Chemokine interactions with receptors trigger signaling pathway activity to form a network fundamental to diverse immune processes, including host homeostasis and responses to disease. Genetic and nongenetic regulation of both the expression and structure of chemokines and receptors conveys chemokine functional heterogeneity. Imbalances and defects in the system contribute to the pathogenesis of a variety of diseases, including cancer, immune and inflammatory diseases, and metabolic and neurological disorders, which render the system a focus of studies aiming to discover therapies and important biomarkers. The integrated view of chemokine biology underpinning divergence and plasticity has provided insights into immune dysfunction in disease states, including, among others, coronavirus disease 2019 (COVID-19). In this review, by reporting the latest advances in chemokine biology and results from analyses of a plethora of sequencing-based datasets, we outline recent advances in the understanding of the genetic variations and nongenetic heterogeneity of chemokines and receptors and provide an updated view of their contribution to the pathophysiological network, focusing on chemokine-mediated inflammation and cancer. Clarification of the molecular basis of dynamic chemokine-receptor interactions will help advance the understanding of chemokine biology to achieve precision medicine application in the clinic.

Background: Various online rumors have led to inappropriate behaviors among the public in response to the COVID-19 epidemic in China. These rumors adversely affect people’s physical and mental health. Therefore, a better understanding of the relationship between public emotions and rumors during the epidemic may help generate useful strategies for guiding public emotions and dispelling rumors.Objective: This study aimed to explore whether public emotions are related to the dissemination of online rumors in the context of COVID-19.Methods: We used the web-crawling tool Scrapy to gather data published by People’s Daily on Sina Weibo, a popular social media platform in China, after January 8, 2020. Netizens’ comments under each Weibo post were collected. Nearly 1 million comments thus collected were divided into 5 categories: happiness, sadness, anger, fear, and neutral, based on the underlying emotional information identified and extracted from the comments by using a manual identification process. Data on rumors spread online were collected through Tencent’s Jiaozhen platform. Time-lagged cross-correlation analyses were performed to examine the relationship between public emotions and rumors.Results: Our results indicated that the angrier the public felt, the more rumors there would likely be (r=0.48, P<.001). Similar results were observed for the relationship between fear and rumors (r=0.51, P<.001) and between sadness and rumors (r=0.47, P<.001). Furthermore, we found a positive correlation between happiness and rumors, with happiness lagging the emergence of rumors by 1 day (r=0.56, P<.001). In addition, our data showed a significant positive correlation between fear and fearful rumors (r=0.34, P=.02).Conclusions: Our findings confirm that public emotions are related to the rumors spread online in the context of COVID-19 in China. Moreover, these findings provide several suggestions, such as the use of web-based monitoring methods, for relevant authorities and policy makers to guide public emotions and behavior during this public health emergency.

A novel haplotype association method is presented, and its power is demonstrated. Relying on a statistical model for linkage disequilibrium (LD), the method first infers ancestral haplotypes and their loadings at each marker for each individual. The loadings are then used to quantify local haplotype sharing between individuals at each marker. A statistical model was developed to link the local haplotype sharing and phenotypes to test for association. We devised a novel method to fit the LD model, reducing the complexity from putatively quadratic to linear (in the number of ancestral haplotypes). Therefore, the LD model can be fitted to all study samples simultaneously, and, consequently, our method is applicable to big data sets. Compared to existing haplotype association methods, our method integrated out phase uncertainty, avoided arbitrariness in specifying haplotypes, and had the same number of tests as the single-SNP analysis. We applied our method to data from the Wellcome Trust Case Control Consortium and discovered eight novel associations between seven gene regions and five disease phenotypes. Among these, GRIK4, which encodes a protein that belongs to the glutamate-gated ionic channel family, is strongly associated with both coronary artery disease and rheumatoid arthritis. A software package implementing methods described in this article is freely available at http://www.haplotype.org.

Noninvasive prenatal screening (NIPS) sequences a mixture of the maternal and fetal cell-free DNA. Fetal trisomy can be detected by examining chromosomal dosages estimated from sequencing reads. The traditional method uses the Z-test, which compares a subject against a set of euploid controls, where the information of fetal fraction is not fully utilized. Here we present a Bayesian method that leverages informative priors on the fetal fraction. Our Bayesian method combines the Z-test likelihood and informative priors of the fetal fraction, which are learned from the sex chromosomes, to compute Bayes factors. Bayesian framework can account for nongenetic risk factors through the prior odds, and our method can report individual positive/negative predictive values. Our Bayesian method has more power than the Z-test method. We analyzed 3,405 NIPS samples and spotted at least 9 (of 51) possible Z-test false positives. Bayesian NIPS is more powerful than the Z-test method, is able to account for nongenetic risk factors through prior odds, and can report individual positive/negative predictive values.

Purpose: To assess the potential added value of Optical Genomic Mapping (OGM) for identifying chromosomal aberrations. Methods: We utilized Optical Genomic Mapping (OGM) to determine chromosomal aberrations in 46 children with B-cell Acute lymphoblastic leukemia ALL (B-ALL) and compared the results of OGM with conventional technologies. Partial detection results were verified by WGS and PCR. Results: OGM showed a good concordance with conventional cytogenetic techniques in identifying the reproducible and pathologically significant genomic SVs. Two new fusion genes (LMNB1::PPP2R2B and TMEM272::KDM4B) were identified by OGM and verified by WGS and RT-PCR for the first time. OGM has a greater ability to detect complex chromosomal aberrations, refine complicated karyotypes, and identify more SVs. Several novel fusion genes and single-gene alterations, associated with definite or potential pathologic significance that had not been detected by traditional methods, were also identified. Conclusion: OGM addresses some of the limitations associated with conventional cytogenomic testing. This all-in-one process allows the detection of most major genomic risk markers in one test, which may have important meanings for the development of leukemia pathogenesis and targeted drugs.

Purpose Fetal fraction (FF) is the percent of cell-free DNA (cfDNA) in the mother’s peripheral blood that is of fetal origin, which plays a pivotal role in noninvasive prenatal screening (NIPS). We present a method that can reliably estimate FFs by examining autosome single-nucleotide polymorphisms (SNPs). Methods Even at a very low sequencing depth, there are plenty of SNPs covered by more than one read. At those SNPs, we define read heterozygosity and demonstrate that the percent of read heterozygosity is a function of FF, which allows FF to be inferred. Results We first demonstrated the effectiveness of our method in inferring FF. Then we used the inferred FF as an informative alternative prior to computing Bayes factors to test for aneuploidy, and observed better power than the Z-test. In analysis of clinical samples, we were able to identify female–male twins thanks to the accurate FF inference. Conclusion Knowing FF improves efficacy of NIPS. It brings a powerful Bayesian method, allows “no call” for samples with small FFs, renders screening for XXY syndrome simpler, and permits an adaptive design to sequence at a higher depth for samples with small FFs.