Identifications of novel genetic signals conferring susceptibility to human complex diseases is pivotal to the disease diagnosis, prevention, and treatment. Genetic association study is a powerful tool to discover candidate genetic signals that contribute to diseases, through statistical tests for correlation between the disease status and genetic variations in study samples. In such studies with a case-control design, a standard practice is to perform the Cochran-Armitage (CA) trend test under an additive genetic model, which suffers from power loss when the model assumption is wrong. The Jonckheere-Terpstra (JT) trend test is an alternative method to evaluate association in a nonparametric way. This study compares the power of the JT trend test and the CA trend test in various scenarios, including different sample sizes (200-2000), minor allele frequencies (0.05-0.4), and underlying modes of inheritance (dominant genetic model to recessive genetic model). By simulation and real data analysis, it is shown that in general the JT trend test has higher, similar, and lower power than the CA trend test when the underlying mode of inheritance is dominant, additive, and recessive, respectively; when the sample size is small and the minor allele frequency is low, the JT trend test outperforms the CA trend test across the spectrum of genetic models. In sum, the JT trend test is a valuable alternative to the CA trend test under certain circumstances with higher statistical power, which could lead to better detection of genetic signals to human diseases and finer dissection of their genetic architecture.

Intercellular communication mediated by cytokines is critical to the development of immune responses, particularly in the context of infectious and inflammatory diseases. By releasing these small molecular weight peptides, the source cells can influence numerous intracellular processes in the target cells, including the secretion of other cytokines downstream. However, there are no readily available bioinformatic resources that can model cytokine-cytokine interactions. In this effort, we built a communication map between major tissues and blood cells that reveals how cytokine-mediated intercellular networks form during homeostatic conditions. We collated the most prevalent cytokines from the literature and assigned the proteins and their corresponding receptors to source tissue and blood cell types based on enriched consensus RNA-Seq data from the Human Protein Atlas database. To assign more confidence to the interactions, we integrated the literature information on cell-cytokine interactions from two systems of immunology databases, immuneXpresso and ImmunoGlobe. From the collated information, we defined two metanetworks: a cell-cell communication network connected by cytokines; and a cytokine-cytokine interaction network depicting the potential ways in which cytokines can affect the activity of each other. Using expression data from disease states, we then applied this resource to reveal perturbations in cytokine-mediated intercellular signalling in inflammatory and infectious diseases (ulcerative colitis and COVID-19, respectively). For ulcerative colitis, with CytokineLink, we demonstrated a significant rewiring of cytokine-mediated intercellular communication between non-inflamed and inflamed colonic tissues. For COVID-19, we were able to identify cell types and cytokine interactions following SARS-CoV-2 infection, highlighting important cytokine interactions that might contribute to severe illness in a subgroup of patients. Such findings have the potential to inform the development of novel, cytokine-targeted therapeutic strategies. CytokineLink is freely available for the scientific community through the NDEx platform and the project github repository.

Ichthyological surveys have traditionally been conducted using whole-specimen, capture-based sampling with varied but conventional fishing gear. Recently, environmental DNA (eDNA) metabarcoding has emerged as a complementary, and possible alternative, approach to whole-specimen methodologies. In the tropics, where much of the diversity remains undescribed, vast reaches continue unexplored, and anthropogenic activities are constant threats; there have been few eDNA attempts for ichthyological inventories. We tested the discriminatory power of eDNA using MiFish primers with existing public reference libraries and compared this with capture-based methods in two distinct ecosystems in the megadiverse Amazon basin. In our study, eDNA provided an accurate snapshot of the fishes at higher taxonomic levels and corroborated its effectiveness to detect specialized fish assemblages. Some flaws in fish metabarcoding studies are routine issues addressed in natural history museums. Thus, by expanding their archives and adopting a series of initiatives linking collection-based research, training and outreach, natural history museums can enable the effective use of eDNA to survey Earth\'s hotspots of biodiversity before taxa go extinct. Our project surveying poorly explored rivers and using DNA vouchered archives to build metabarcoding libraries for Neotropical fishes can serve as a model of this protocol.

Nasal and nasopharyngeal swab specimens tested by the Cepheid Xpert Xpress SARS-CoV-2 were analyzed by whole-genome sequencing based on impaired detection of the N2 target. Each viral genome had at least one mutation in the N gene, which likely arose independently in the New York City and Pittsburgh study sites.

High availability of fast, cheap, and high-throughput next generation sequencing techniques resulted in acquisition of numerous de novo sequenced and assembled bacterial genomes. It rapidly became clear that digging out useful biological information from such a huge amount of data presents a considerable challenge. In this chapter we share our experience with utilization of several handy open source comparative genomic tools. All of them were applied in the studies focused on revealing inter- and intraspecies variation in pectinolytic plant pathogenic bacteria classified to Dickeya solani and Pectobacterium parmentieri. As the described software performed well on the species within the Pectobacteriaceae family, it presumably may be readily utilized on some closely related taxa from the Enterobacteriaceae family. First of all, implementation of various annotation software is discussed and compared. Then, tools computing whole genome comparisons including generation of circular juxtapositions of multiple sequences, revealing the order of synteny blocks or calculation of ANI or Tetra values are presented. Besides, web servers intended either for functional annotation of the genes of interest or for detection of genomic islands, plasmids, prophages, CRISPR/Cas are described. Last but not least, utilization of the software designed for pangenome studies and the further downstream analyses is explained. The presented work not only summarizes broad possibilities assured by the comparative genomic approach but also provides a user-friendly guide that might be easily followed by nonbioinformaticians interested in undertaking similar studies.

BACKGROUND: Type 2 diabetes (T2D) increases the risk of many types of cancer. Dysregulation of proteasome-related protein degradation leads to tumorigenesis, while Exendin-4, a glucagon-like peptide 1 receptor (GLP-1R) agonist, possesses anti-cancer effects.
METHODS: We explored the co-expression of proteasome alpha 2 subunit (PSMA2) and GLP-1R in the Cancer Genome Atlas (TCGA) database and human cervical cancer specimens, supplemented by in vivo and in vitro studies using multiple cervical cancer cell lines.
RESULTS: PSMA2 expression was increased in 12 cancer types in TCGA database and cervical cancer specimens from patients with T2D (T2D vs non-T2D: 3.22 (95% confidence interval CI: 1.38, 5.05) vs 1.00 (0.66, 1.34) fold change, P = 0.01). psma2-shRNA decreased cell proliferation in vitro, and tumour volume and Ki67 expression in vivo. Exendin-4 decreased psma2 expression, tumour volume and Ki67 expression in vivo. There was no change in GLP-1R expression in 12 cancer types in TCGA database. However, GLP-1R expression (T2D vs non-T2D: 5.49 (3.0, 8.1) vs 1.00 (0.5, 1.5) fold change, P < 0.001) was increased and positively correlated with PSMA2 expression in T2D-related (r = 0.68)  but not in non-T2D-related cervical cancer specimens. This correlation was corroborated by in vitro experiments where silencing glp-1r decreased psma2 expression. Exendin-4 attenuated phospho-p65 and -IκB expression in the NF-κB pathway.
CONCLUSIONS: PSMA2 and GLP-1R expression in T2D-related cervical cancer specimens was increased and positively correlated, suggesting hyperglycaemia might promote cancer growth by increasing PSMA2 expression which could be attenuated by Exendin-4.
BACKGROUND: This project was supported by Postdoctoral Fellowship Scheme, Direct Grant, Diabetes Research and Education Fund from the Chinese University of Hong Kong (CUHK).

An increasing number of \'-omics\' datasets, generated by labs all across the world, are becoming available. They contain a wealth of data that are largely unexplored. Not every scientist, however, will have access to the required resources and expertise to analyze such data from scratch. Fortunately, a growing number of investigators is dedicating their time and effort to the development of user friendly, online applications that allow researchers to use and investigate these datasets. Here, we will illustrate the usefulness of such an approach. Using regulation of Wnt7b expression as an example, we will highlight a selection of accessible tools and resources that are available to researchers in the area of mammary gland biology. We show how they can be used for in silico analyses of gene regulatory mechanisms, resulting in new hypotheses and providing leads for experimental follow up. We also call out to the mammary gland community to join forces in a coordinated effort to generate and share additional tissue-specific \'-omics\' datasets and thereby expand the in silico toolbox.

暂无摘要。

Although it is known that inflammation is involved in Parkinson\'s disease (PD) pathogenesis and vitamin K2 (VK2) has anti-inflammatory effects, to date few studies have been reported on the relationship between VK2 and PD development. Herein we presented a case-control study involving 93 PD patients and 95 healthy controls. Overall, the serum VK2 level of PD patients (3.49 ± 1.68 ng/ml) was significantly lower than that of healthy controls (5.77 ± 2.71 ng/ml). When the PD patients were stratified by disease progression, we observed that the serum VK2 level of late stage patients was further decreased to 3.15 ± 1.18 ng/ml while the serum VK2 level of early stage patients was 3.92 ± 2.09 ng/ml. Furthermore, the curve analysis showed that the serum VK2 level decreased gradually with the increment of PD Hoehn-Yahr (H-Y) stage. We also confirmed the dysregulated inflammatory responses and coagulation cascades in PD patients by public dataset, which are associated to the decreased VK2 level. In summary, we found the serum VK2 level in PD patients is lower than that in healthy controls. The decrease of VK2 level may be related to the occurrence and progression of PD by loosening the regulation of inflammatory responses and coagulation cascades signal.

The reconstruction of relationships within recently radiated groups is challenging even when massive amounts of sequencing data are available. The use of restriction site-associated DNA sequencing (RAD-Seq) to this end is promising. Here, we assessed the performance of RAD-Seq to infer the species-level phylogeny of the rapidly radiating genus Cereus (Cactaceae). To examine how the amount of genomic data affects resolution in this group, we used datasets and implemented different analyses. We sampled 52 individuals of Cereus, representing 18 of the 25 species currently recognized, plus members of the closely allied genera Cipocereus and Praecereus, and other 11 Cactaceae genera as outgroups. Three scenarios of permissiveness to missing data were carried out in iPyRAD, assembling datasets with 30% (333 loci), 45% (1440 loci), and 70% (6141 loci) of missing data. For each dataset, Maximum Likelihood (ML) trees were generated using two supermatrices, i.e., only SNPs and SNPs plus invariant sites. Accuracy and resolution were improved when the dataset with the highest number of loci was used (6141 loci), despite the high percentage of missing data included (70%). Coalescent trees estimated using SVDQuartets and ASTRAL are similar to those obtained by the ML reconstructions. Overall, we reconstruct a well-supported phylogeny of Cereus, which is resolved as monophyletic and composed of four main clades with high support in their internal relationships. Our findings also provide insights into the impact of missing data for phylogeny reconstruction using RAD loci.