Explore the vast range of titles available.

Fine-mapping to plausible causal variation may be more effective in multi-ancestry cohorts, particularly in the MHC, which has population-specific structure. To enable such studies, we constructed a large ( n  = 21,546) HLA reference panel spanning five global populations based on whole-genome sequences. Despite population-specific long-range haplotypes, we demonstrated accurate imputation at G-group resolution (94.2%, 93.7%, 97.8% and 93.7% in admixed African (AA), East Asian (EAS), European (EUR) and Latino (LAT) populations). Applying HLA imputation to genome-wide association study data for HIV-1 viral load in three populations (EUR, AA and LAT), we obviated effects of previously reported associations from population-specific HIV studies and discovered a novel association at position 156 in HLA-B. We pinpointed the MHC association to three amino acid positions (97, 67 and 156) marking three consecutive pockets (C, B and D) within the HLA-B peptide-binding groove, explaining 12.9% of trait variance. A high-resolution reference panel based on whole-genome sequencing data enables accurate imputation of HLA alleles across diverse populations and fine-mapping of HLA association signals for HIV-1 host response.

Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2 , TMPRSS2 and CTSL across 107 single-cell RNA-sequencing studies from different tissues. ACE2 , TMPRSS2 and CTSL are coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2 , TMPRSS2 and CTSL . Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2 + TMPRSS2 + cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial–macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention. An integrated analysis of over 100 single-cell and single-nucleus transcriptomics studies illustrates severe acute respiratory syndrome coronavirus 2 viral entry gene coexpression patterns across different human tissues, and shows association of age, smoking status and sex with viral entry gene expression in respiratory cell populations.

The interactions of microorganisms among themselves and with their multicellular host take place at the microscale, forming complex networks and spatial patterns. Existing technology does not allow the simultaneous investigation of spatial interactions between a host and the multitude of its colonizing microorganisms, which limits our understanding of host–microorganism interactions within a plant or animal tissue. Here we present spatial metatranscriptomics (SmT), a sequencing-based approach that leverages 16S/18S/ITS/poly-d(T) multimodal arrays for simultaneous host transcriptome- and microbiome-wide characterization of tissues at 55-µm resolution. We showcase SmT in outdoor-grown Arabidopsis thaliana leaves as a model system, and find tissue-scale bacterial and fungal hotspots. By network analysis, we study inter- and intrakingdom spatial interactions among microorganisms, as well as the host response to microbial hotspots. SmT provides an approach for answering fundamental questions on host–microbiome interplay. Interactions among plant hosts, bacteria and fungi are measured at high spatial resolution.

ABSTRACT Genetic interaction (GI) analysis is a powerful genetic strategy that analyzes the fitness and phenotypes of single- and double-gene mutant cells in order to dissect the epistatic interactions between genes, categorize genes into biological pathways, and characterize genes of unknown function. GI analysis has been extensively employed in model organisms for foundational, systems-level assessment of the epistatic interactions between genes. More recently, GI analysis has been applied to microbial pathogens and has been instrumental for the study of clinically important infectious organisms. Here, we review recent advances in systems-level GI analysis of diverse microbial pathogens, including bacterial and fungal species. We focus on important applications of GI analysis across pathogens, including GI analysis as a means to decipher complex genetic networks regulating microbial virulence, antimicrobial drug resistance and host–pathogen dynamics, and GI analysis as an approach to uncover novel targets for combination antimicrobial therapeutics. Together, this review bridges our understanding of GI analysis and complex genetic networks, with applications to diverse microbial pathogens, to further our understanding of virulence, the use of antimicrobial therapeutics and host−pathogen interactions.  This review bridges our understanding of genetic interaction analysis and complex genetic networks, with applications to diverse microbial pathogens, to further our understanding of virulence, the use of antimicrobial therapeutics and host–pathogen interactions.

During the last 20 years, a new field of research delineating the importance of the microbiota in health and diseases has emerged. Inappropriate host-microbiota interactions have been shown to trigger a wide range of chronic inflammatory diseases, and defining the exact mechanisms behind perturbations of such relationship, as well as ways by which these disturbances can lead to disease states, both remain to be fully elucidated. The mucosa-associated microbiota constitutes a recently studied microbial population closely linked with the promotion of chronic intestinal inflammation and associated disease states. This review will highlight seminal works that have brought into light the importance of the mucosa-associated microbiota in health and diseases, emphasizing the challenges and promises of expending the mucosal microbiology field of research.

The gut microbiota is vital to host health and, as such, it is important to elucidate the mechanisms altering its composition and diversity. Intestinal helminths are host immunomodulators and have evolved both temporally and spatially in close association with the gut microbiota, resulting in potential mechanistic interplay. Host–helminth and host–microbiota interactions are comparatively well-examined, unlike microbiota–helminth relationships, which typically focus on experimental infection with a single helminth species in laboratory animals. Here, in addition to a review of the literature on helminth–microbiota interactions, we examined empirically the association between microbiota diversity and composition and natural infection of multiple helminth species in wild mice (Apodemus flavicollis), using 16S rRNA gene catalogues (metataxonomics). In general, helminth presence is linked with high microbiota diversity, which may confer health benefits to the host. Within our wild rodent system variation in the composition and abundance of gut microbial taxa associated with helminths was specific to each helminth species and occurred both up- and downstream of a given helminth's niche (gut position). The most pronounced helminth–microbiota association was between the presence of tapeworms in the small intestine and increased S24–7 (Bacteroidetes) family in the stomach. Helminths clearly have the potential to alter gut homeostasis. Free-living rodents with a diverse helminth community offer a useful model system that enables both correlative (this study) and manipulative inference to elucidate helminth–microbiota interactions.

In recent years, with the advent of next-generation sequencing along with the development of various bioinformatics tools, RNA sequencing (RNA-Seq)-based transcriptome analysis has become much more affordable in the field of biological research. This technique has even opened up avenues to explore the transcriptome of non-model organisms for which a reference genome is not available. This has made fish health researchers march towards this technology to understand pathogenic processes and immune reactions in fish during the event of infection. Recent studies using this technology have altered and updated the previous understanding of many diseases in fish. RNA-Seq has been employed in the understanding of fish pathogens like bacteria, virus, parasites, and oomycetes. Also, it has been helpful in unraveling the immune mechanisms in fish. Additionally, RNA-Seq technology has made its way for future works, such as genetic linkage mapping, quantitative trait analysis, disease-resistant strain or broodstock selection, and the development of effective vaccines and therapies. Until now, there are no reviews that comprehensively summarize the studies which made use of RNA-Seq to explore the mechanisms of infection of pathogens and the defense strategies of fish hosts. This review aims to summarize the contemporary understanding and findings with regard to infectious pathogens and the immune system of fish that have been achieved through RNA-Seq technology.

The interaction between the human microbiome and immune system has an effect on several human metabolic functions and impacts our well-being. Additionally, the interaction between humans and microbes can also play a key role in determining the wellness or disease status of the human body. Dysbiosis is related to a plethora of diseases, including skin, inflammatory, metabolic, and neurological disorders. A better understanding of the host-microbe interaction is essential for determining the diagnosis and appropriate treatment of these ailments. The significance of the microbiome on host health has led to the emergence of new therapeutic approaches focused on the prescribed manipulation of the host microbiome, either by removing harmful taxa or reinstating missing beneficial taxa and the functional roles they perform. Culturing large numbers of microbial taxa in the laboratory is problematic at best, if not impossible. Consequently, this makes it very difficult to comprehensively catalog the individual members comprising a specific microbiome, as well as understanding how microbial communities function and influence host-pathogen interactions. Recent advances in sequencing technologies and computational tools have allowed an increasing number of metagenomic studies to be performed. These studies have provided key insights into the human microbiome and a host of other microbial communities in other environments. In the present review, the role of the microbiome as a therapeutic agent and its significance in human health and disease is discussed. Advances in high-throughput sequencing technologies for surveying host-microbe interactions are also discussed. Additionally, the correlation between the composition of the microbiome and infectious diseases as described in previously reported studies is covered as well. Lastly, recent advances in state-of-the-art bioinformatics software, workflows, and applications for analysing metagenomic data are summarized.

The emergence of genome-scale forward genetic screening techniques, such as Haploid Genetic screen and clustered regularly interspaced short palindromic repeats (CRISPR) knockout screen has opened new horizons in our understanding of virus infection biology. CRISPR screening has become a popular tool for the discovery of novel host factors for several viruses due to its specificity and efficiency in genome editing. Here, we review how CRISPR screening has revolutionized our understanding of virus-host interactions from scientific and technological viewpoints. A summary of the published screens conducted thus far to uncover virus host factors is presented, highlighting their experimental design and significant findings. We will outline relevant methods for customizing the CRISPR screening process to answer more specific hypotheses and compile a glossary of conducted CRISPR screens to show their design aspects. Furthermore, using flaviviruses and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as examples, we hope to offer a broad-based perspective on the capabilities of CRISPR screening to serve as a reference point to guide future unbiased discovery of virus host factors.

Protein and genetic interaction maps can reveal the overall physical and functional landscape of a biological system. To date, these interaction maps have typically been generated under a single condition, even though biological systems undergo differential change that is dependent on environment, tissue type, disease state, development or speciation. Several recent interaction mapping studies have demonstrated the power of differential analysis for elucidating fundamental biological responses, revealing that the architecture of an interactome can be massively re‐wired during a cellular or adaptive response. Here, we review the technological developments and experimental designs that have enabled differential network mapping at very large scales and highlight biological insight that has been derived from this type of analysis. We argue that differential network mapping, which allows for the interrogation of previously unexplored interaction spaces, will become a standard mode of network analysis in the future, just as differential gene expression and protein phosphorylation studies are already pervasive in genomic and proteomic analysis. Protein and genetic interaction maps have typically been generated under a single condition, providing a static view of the interactome. Recent studies employing differential analysis, however, have revealed that widespread re‐wiring of the interactome underlies key biological responses.