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INTRODUCTION Small intestine bacterial overgrowth (SIBO) is common in children from low-income countries and has been cross-sectionally associated with growth stunting. We sought to determine whether SIBO was associated with poor growth and neurodevelopmental in a longitudinal analysis. METHODS We measured SIBO by glucose hydrogen breath test (GHBT) at 18, 52, 78, and 104 weeks of life in a prospective longitudinal birth cohort of Bangladeshi children. Sociodemographic information and measures of enteric inflammation were analyzed as covariates. Diarrheal samples were tested for enteropathogens using polymerase chain reaction. Regression models were created using standardized mean GHBT area under the H2 curve (AUC) to determine associations with linear growth and cognitive, language, and motor scores on the Bayley-III Scales of Infant and Toddler Development at 2 years. We also investigated associations between GHBT AUC and enteropathogen exposure. RESULTS A 1-ppm increase in standardized mean GHBT AUC was associated with a 0.01-SD decrease in length-for-age Z score (P = 0.03) and a 0.11-point decrease in Bayley language score (P = 0.05) at 2 years of age in adjusted analysis. Enteroaggregative Escherichia coli, Enteropathogenic Escherichia coli, Giardia, and Enterocytozoon bieneusi were associated with increased GHBT AUC, whereas Clostridium difficile, norovirus GI, sapovirus, rotavirus, and Cryptosporidium were associated with decreased GHBT AUC. None were consistent across all 4 time points. DISCUSSION SIBO in the first 2 years of life is associated with growth stunting and decreased language ability in Bangladeshi infants and may represent a modifiable risk factor in poor growth and neurodevelopment in low-income countries.

Pseudomonas aeruginosa rugose small-colony variants (RSCVs) are frequently isolated from chronic infections, yet they are rarely reported in environmental isolates. Here, during comparative genomic analysis of two P. aeruginosa strains isolated from crude oil, we discovered a spontaneous in-frame deletion, wspAΔ280-307 , which led to hyper-biofilm and RSCV phenotypes. WspA is a homolog of methyl-accepting chemotaxis proteins (MCPs) that senses surfaces to regulate biofilm formation by stimulating cyclic-di-guanosine monophosphate (c-di-GMP) synthesis through the Wsp system. However, the methylation sites of WspA have never been identified. In this study, we identified E280 and E294 of WspA as methylation sites. The wspAΔ280-307 mutation enabled the Wsp system to lock into a constitutively active state that is independent of regulation by methylation. The result is an enhanced production of c-di-GMP. Sequence alignment revealed three conserved repeat sequences within the amino acid residues 280 to 313 (aa280-313) region of WspA homologues, suggesting that a spontaneous deletion within this DNA encoding region was likely a result of intragenic recombination and that similar mutations might occur in several related bacterial genera. Our results provide a plausible explanation for the selection of RSCVs and a mechanism to confer a competitive advantage for P. aeruginosa in a crude-oil environment. This article is protected by copyright. All rights reserved.

A new species similar to Bryocrumia vivicolor, the only known species in the genus Bryocrumia, is described as Bryocrumia malabarica spec. nova from the Malabar Wildlife Sanctuary in the Western Ghats of Kerala in Peninsular India. It resembles Homalia in external appearance and was collected in a rheophytic habitat along a stream channel in the evergreen forest. The new species is characterised by closely arranged leaves with distinct tricostate, ovate-rounded to truncate leaves, upper margin of leaf rounded with fine serrations and an inconspicuous central strand in stem cross section.

Cryptosporidiosis is common in early childhood, and both diarrheal and subclinical infections are associated with adverse developmental outcomes. Improved therapeutic medications may help reduce the burden of cryptosporidial diarrhea; however, an effective vaccine would be better able to prevent the detrimental impact of both diarrheal and subclinical disease. A more complete understanding of naturally occurring immunity may further inform strategies to develop an effective vaccine. In this prospective cohort study of Bangladeshi children, greater fecal IgA at 12 months, but not plasma IgG, directed against two sporozoite-expressed, immunodominant and vaccine candidate antigens was associated with delayed time to subsequent cryptosporidiosis to 3 years of life. These findings extend prior work and further support the role of mucosal antibody responses in naturally developing protective immunity to Cryptosporidium.

The Y chromosome plays key roles in male fertility and reflects the evolutionary history of paternal lineages. Here, we present a de novo genome assembly of the Hu sheep with the first draft assembly of ovine Y chromosome (oMSY), using nanopore sequencing and Hi-C technologies. The oMSY that we generated spans 10.6 Mb from which 775 Y-SNPs were identified by applying a large panel of whole genome sequences from worldwide sheep and wild Iranian mouflons. Three major paternal lineages (HY1a, HY1b and HY2) were defined across domestic sheep, of which HY2 was newly detected. Surprisingly, HY2 forms a monophyletic clade with the Iranian mouflons and is highly divergent from both HY1a and HY1b. Demographic analysis of Y chromosomes, mitochondrial and nuclear genomes confirmed that HY2 and the maternal counterpart of lineage C represented a distinct wild mouflon population in Iran that diverge from the direct ancestor of domestic sheep, the wild mouflons in Southeastern Anatolia. Our results suggest that wild Iranian mouflons had introgressed into domestic sheep and thereby introduced this Iranian mouflon specific lineage carrying HY2 to both East Asian and Africa sheep populations.

In a cohort of infants, we found that lack of the Lewis histo-blood group antigen was associated with increased susceptibility to shigellosis. Broadly inhibiting fucosylation in epithelial cells in vitro decreased invasion by Shigella flexneri. These results support a role for fucosylated glycans in susceptibility to shigellosis.

Pseudomonas aeruginosa isolates from cystic fibrosis patients are often mucoid (due to the overexpression of exopolysaccharide alginate) yet lost motility. It remains unclear about how P. aeruginosa coordinately regulates alginate production and the type IV pili-driven twitching motility. Here we showed that sigma 22 factor (AlgT/U), an activator of alginate biosynthesis, repressed twitching motility by inhibiting the expression of pilin (PilA) through the intermediate transcriptional regulator AmrZ, which directly bound to the promoter region of pilA in both mucoid strain FRD1 and non-mucoid strain PAO1. Four conserved AmrZ-binding sites were found in pilA promoters among 10 P. aeruginosa strains although their entire pilA promoters had low identity. AmrZ has been reported to be essential for twitching in PAO1. We found that AmrZ was also required for twitching in mucoid FRD1, yet a high level of AmrZ inhibited twitching motility. This result was consistent with the phenomenon that twitching is frequently repressed in mucoid strains, in which the expression of AmrZ was highly activated by AlgT. Additionally, AlgT also inhibited the transcription of pilMNOP operon, which is involved in efficient pilus assembly. Our data elucidated a mechanism for how AlgT and AmrZ coordinately controlled twitching motility in P. aeruginosa.

The prevalence of smoking is significantly higher in persons with schizophrenia (SCZ) than in the general population. However, the biological mechanisms of the comorbidity of smoking and SCZ are largely unknown. This study aimed to reveal shared biological pathways for the two diseases by analyzing data from two genome-wide association studies with a total sample size of 153,898. With pathway-based analysis, we first discovered 18 significantly enriched pathways shared by SCZ and smoking, which were classified into five groups: postsynaptic density, cadherin binding, dendritic spine, long-term depression, and axon guidance. Then, by using an integrative analysis of genetic, epigenetic, and expression data, we found not only 34 critical genes (e.g., PRKCZ, ARHGEF3, and CDKN1A) but also various risk-associated SNPs in these genes, which convey susceptibility to the comorbidity of the two disorders. Finally, using both in vivo and in vitro data, we demonstrated that the expression profiles of the 34 genes were significantly altered by multiple psychotropic drugs. Together, this multi-omics study not only reveals target genes for new drugs to treat SCZ but also reveals new insights into the shared genetic vulnerabilities of SCZ and smoking behaviors.

Bacteria usually live in densely packed communities called biofilms, where interactions between the bacteria give rise to complex properties. Quantitative analysis is indispensable in understanding those properties. However, current biofilm culturing approaches impose various limitations to these types of analysis. Here, we developed a microfluidic approach for quantitative study of biofilms, which is universal and can be used to culture biofilms of various bacterial species. To demonstrate the advantages of this approach, we present two examples, both of which revealed new biological insights. In the first example, we explored the response of Escherichia coli biofilms to exogenous hydrogen peroxide; We found the biofilms gained resistance to H2O2, but their growth was slowed down due to the metabolic cost of maintaining the resistance; However, under oxygen limitation, H2O2 can anti-intuitively boost biofilm growth. In the second example, we explored resource retention by Pseudomonas aeruginosa biofilms; We observed a fluorescent substance within the biofilm and identified it as the siderophore pyoverdine; We further showed that the extracellular matrix component Psl acted as a retention barrier for pyoverdine, minimizing its loss into the environment and therefore potentially promoting sharing of pyoverdine within the biofilm.

Histamine is a bioactive amine associated with a plethora of normal and pathophysiological processes, with the latter being dependent on both genetic and environmental factors including infectious agents. Previously, we showed in mice that susceptibility to Bordetella pertussis and pertussis toxin (PTX) induced histamine sensitization (Bphs) is controlled by histamine receptor H1 (Hrh1/HRH1) alleles. Bphs susceptible and resistant alleles (Bphss/Bphsr) encode for two-conserved protein haplotypes. Given the importance of HRH1 signaling in health and disease, we sequenced Hrh1 across an extended panel of laboratory and wild-derived inbred strains and phenotyped them for Bphs. Unexpectedly, eight strains homozygous for the Bphsr allele phenotyped as Bphss, suggesting the existence of a modifying locus segregating among the strains capable of complementing Bphsr. Genetic analyses mapped this modifier locus to mouse chromosome 6; designated Bphs-enhancer (Bphse), within a functional linkage disequilibrium domain encoding multiple loci controlling responsiveness to histamine (Bphs/Hrh1 and Histh1-4). Interval-specific single-nucleotide polymorphism (SNP) based association testing across 50 laboratory and wild-derived inbred mouse strains and functional prioritization analyses resulted in the identification of candidate genes for Bphse within a ∼5.5 Mb interval (Chr6:111.0-116.4 Mb), including Atg7, Plxnd1, Tmcc1, Mkrn2, Il17re, Pparg, Lhfpl4, Vgll4, Rho and Syn2. Taken together, these results demonstrate the power of combining network-based computational methods with the evolutionarily significant diversity of wild-derived inbred mice to identify novel genetic mechanisms controlling susceptibility and resistance to histamine shock.

Genetic mapping is a primary tool of genetics in model organisms; however, many quantitative trait loci (QTL) contain tens or hundreds of positional candidate genes. Prioritizing these genes for validation is often ad hoc and biased by previous findings. Here we present a technique for computationally prioritizing positional candidates based on computationally inferred gene function. Our method uses machine learning with functional genomic networks, whose links encode functional associations among genes, to identify network-based signatures of functional association to a trait of interest. We demonstrate the method by functionally ranking positional candidates in a large locus on mouse Chr 6 (45.9 Mb to 127.8 Mb) associated with histamine hypersensitivity (Hhs). Hhs is characterized by systemic vascular leakage and edema in response to histamine challenge, which can lead to multiple organ failure and death. Although Hhs risk is strongly influenced by genetics, little is known about its underlying molecular or genetic causes, due to genetic and physiological complexity of the trait. To dissect this complexity, we ranked genes in the Hhs locus by predicting functional association with multiple Hhs-related processes. We integrated these predictions with new single nucleotide polymorphism (SNP) association data derived from a survey of 23 inbred mouse strains and congenic mapping data. The top-ranked genes included Cxcl12, Ret, Cacna1c, and Cntn3, all of which had strong functional associations and were proximal to SNPs segregating with Hhs. These results demonstrate the power of network-based computational methods to nominate highly plausible quantitative trait genes even in highly challenging cases involving large QTLs and extreme trait complexity.

Microbial reduction of Fe(III) is an important biogeochemical process in anoxic or acidic environments. However, this process under oxic and pH-neutral conditions is understudied, and the iron reduction capability of the phylum Actinobacteria is poorly known. In this study, we investigated the capacity of diverse actinobacteria to reduce Fe(III) in the presence of oxygen and at pH 7. From 277 actinobacterial strains isolated from deep sea and terrestrial soil, 109 strains (39.40%) belonging to 17 genera showed the capacity of reducing FeCl3 under oxic and initially neutral pH conditions. Among the 109 strains, 49 spanning 13 genera also reduced ferric iron oxides under the same conditions. Four non-filamentous strains from four genera were selected for further analyses. All the four strains showed the capacity to reduce goethite under oxic and pH 7 and 8 conditions. Microbial fuel cell (MFC) experiments showed a current enhancement when the strains were cultured in the presence of goethite. Living actinobacterial cells and their metabolites played a key role in reducing Fe(III). The strains produced organic acids and siderophores, which likely promoted Fe(III) reduction due to altered reduction potentials of complexed Fe(III) and inhibition of Fe(II) oxidization by oxygen. SEM/EDS and XRD analyses detected vivianite in the culture of Kocuria oceani FXJ8.057 with goethite, where, unexpectedly, K. oceani FXJ8.057 produced nanowire-like structures. These results demonstrate that actinobacteria are able to reduce Fe(III) in both aqueous and solid forms under oxic and pH-neutral conditions. The mechanisms, however, still need further investigation.

Background Bisphenol A is widely used in the manufacture of polycarbonate plastics and has caused increasing concern over its potential adverse impacts on spermatogenesis. However, the effect of bisphenol A on spermiogenesis is yet to be explored. Objectives To evaluate whether bisphenol A has adverse effects on DNA integrity and protamination of spermatogenic cell. Materials and methods Newborn male mice were subcutaneously injected with bisphenol A (0.1, 5 mg/kg body weight, n = 15) or coin oil (control group, n = 20) daily from post-natal day 1 until 35. At post-natal day 70, epididymis caudal spermatozoa and testes were collected. Sperm count, sperm motility, and sperm morphology were analyzed. The sperm chromatin structure assay was performed to examine the sperm DNA fragmentation. Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) method was used to assess apoptosis of spermatogenic cells. The ultrastructural features of testicular sections were examined under a transmission electron microscope. Western blot and RT-PCR were used to detect the expression levels of transition protein (Tnp) 1 and Tnp2, protamine (Prm) 1 and Prm2 protein, and mRNA in mice testes. Results Bisphenol A significantly reduced sperm counts, impaired sperm motility, and increased the percentage of malformed spermatozoa. Poor sperm chromatin integrity and increased TUNEL-positive spermatogenic cells were also observed in mice exposed to bisphenol A. Ultrastructural analysis of testes showed that bisphenol A exposure caused incomplete chromatin condensation, retention of residual cytoplasm, and abnormal acrosome formation. In addition, the relative expression levels of Tnp2 and Prm2 in mice testes decreased significantly in bisphenol A groups. Discussion and conclusion Our findings identified that neonatal bisphenol A exposure may negatively contribute to the sperm quality in adult mice. Mechanistically, we showed that bisphenol A reduced sperm chromatin integrity along with increased DNA damage, which may be due to poor protamination of spermatozoa.

Our previous genome-wide association study identified 83 genome-wide significant SNPs and 20 novel promising candidate genes for milk fatty acids in Chinese Holstein. Among them, the enoyl-CoA hydratase, short chain 1 (ECHS1) and enoyl-CoA hydratase and 3-hydroxyacyl CoA dehydrogenase (EHHADH) genes were located near two SNPs and one SNP respectively, and they play important roles in fatty acid metabolism pathways. We herein validated whether the two genes have genetic effects on milk fatty acid traits in dairy cattle. By re-sequencing the full-length coding region, partially adjacent introns and 3000 bp up/downstream flanking sequences, we identified 12 SNPs in ECHS1: two in exons, four in the 3' flanking region and six in introns. The g.25858322C>T SNP results in an amino acid replacement from leucine to phenylalanine and changes the secondary structure of the ECHS1 protein, and single-locus association analysis showed that it was significantly associated with three milk fatty acids (P = 0.0002-0.0013). The remaining 11 SNPs were found to be significantly associated with at least one milk fatty acid (P =

Human epithelial cells can be infected by more than 200 types of human papilloma viruses (HPVs), and persistent HPV infections lead to cervical cancer or other deadly cancers. It has been established that mitotic progression is critical for HPV16 infection, but the underlying mechanism remains unknown. Here, we report that oncoprotein E7 of HPV16 but not HPV18 retards mitotic progression in host cell by direct binding to the C terminus of Microtubule-Associated Protein 4 (MAP4). MAP4 is a novel bona fide target of HPV16E7 protein which binds and recruits the latter to spindle microtubule in mitosis. HPV16E7 protein promotes MAP4 stability by inhibiting MAP4 phosphorylation- mediated degradation to increase the stability of microtubule polymerization and cause an extend mitotic progression. We further uncovered that Mps1 is a kinase of MAP4, and E7-MAP4 binding blocks Mps1 phosphorylation of MAP4, thereby interrupting phosphorylation-dependent MAP4 degradation. Mutations of MAP4 at T927ES928E partially abolished E7-binding capacity and rescued mitotic progression in host cells. In conclusion, our study reveals a molecular mechanism by which HPV16E7 perturbs host mitotic progression by interfering Mps1-MAP4 signaling cascade, which results in an extended infection window and may facilitate the persistent HPV16 infection.

Biofilms are microbial communities that are embedded in the extracellular matrix. The exopolysaccharide (EPS) is a key component of the biofilm matrix that maintains the structure of the biofilm and protects the bacteria from antimicrobials. Microbial glycoside hydrolases have been exploited to disrupt biofilms by breaking down EPSs. PssZ has recently been identified as a glycoside hydrolase that can disperse aggregates of Listeria monocytogenes. In this study, the crystal structure of PssZ has been determined at 1.6 Å resolution. PssZ belongs to glycoside hydrolase family 8 and adopts a classical (α/α)6-barrel fold. This architecture forms a deep groove which may serve as the substrate-binding pocket. The conserved catalytic residues (Glu72, Trp110, Asn119, Phe167, Tyr183 and Asp232) are localized at the centre of the groove. This crystal structure will help to improve the understanding of the hydrolytic mechanism of PssZ and its application as a biofilm disrupter.

In this prospective cohort study of Bangladeshi children, greater fecal immunoglobulin A, but not plasma immunoglobulin G, directed against the Cryptosporidium sporozoite-expressed antigen Cp23 at 12 months of age was associated with delayed time to subsequent cryptosporidiosis. This finding suggests a protective role for mucosal antibody-mediated immunity in naturally exposed children.

Limited sampling means that relatively little is known about the diversity and evolutionary history of mammalian members of the Hepadnaviridae (genus Orthohepadnavirus). An important case in point are shrews, the fourth largest group of mammals, but for which there is limited knowledge on the role they play in viral evolution and emergence. Here, we report the discovery of a novel shrew hepadnavirus. The newly discovered virus, denoted shrew hepatitis B virus (SHBV), is divergent to be considered a new species of Orthohepadnavirus. Phylogenetic analysis revealed that these viruses were usually most closely related to TBHBV (tent-making bat hepatitis B virus), known to be able to infect human hepatocytes, and had a similar genome structure, although SHBV fell in a more basal position in the surface protein phylogeny. In sum, these data suggest that shrews are natural hosts for hepadnaviruses and may have played an important role in their long-term evolution., Highlights • A highly divergent hepadnavirus was identified in shrews in China. • The shrew virus represents a novel species of mammalian orthohepadnaviruses. • The shrew virus grouped with TBHBV in some genes, previously shown to be able to infect human hepatocytes. • Cross-species virus transmission occurred among the three shrew species.

One of the hallmarks of the environmental bacterium Pseudomonas aeruginosa is its excellent ecological flexibility, which can thrive in diverse ecological niches. In different ecosystems, P. aeruginosa may use different strategies to survive, such as forming biofilms in crude oil environment, converting to mucoid phenotype in the cystic fibrosis (CF) lung, or becoming persisters when treated with antibiotics. Rugose small colony variants (RSCVs) are the adaptive mutants of P. aeruginosa, which can be frequently isolated from chronic infections. During the past years, there has been a renewed interest in using P. aeruginosa as a model organism to investigate the RSCVs formation, persistence and pathogenesis, as RSCVs represent a hyper-biofilm formation, high adaptability, high-tolerance sub-population in biofilms. This review will briefly summarize recent advances regarding the phenotypic, genetic and host interaction associated with RSCVs, with an emphasis on P. aeruginosa. Meanwhile, some non-pathogenic bacteria such as Pseudomonas fluorescence, Pseudomonas putida and Bacillus subtilis will be also included. Remarkable emphasis is given on intrinsic functions of such hyper-biofilm formation characteristic as well as its potential applications in several biocatalytic transformations including wastewater treatment, microbial fermentation, and plastic degradation. Hopefully, this review will attract the interest of researchers in various fields and shape future research focused not only on evolutionary biology but also on biotechnological applications related to RSCVs.

Streptomyces is a model filamentous prokaryote to study multicellular differentiation and a rich reservoir for antibiotics discovery. In their natural conditions, Streptomyces grows at the interface of porous soil, air, and water. The morphological development of Streptomyces is traditionally performed on agar plates and mostly studied at the population levels. However, the detailed lifecycle of Streptomyces has not been well studied due to its complexity and lack of research tools which can mimic their natural conditions in the soil. Here, we developed a simple assembled microfluidic device for cultivation and the entire lifecycle observation of Streptomyces development from single-cell level. The microfluidic device composed of a microchannel for loading samples and supplying nutrients, microwell arrays for seeding and growth of single spores, and air-filled chambers aside of the microwells that facilitate growth of aerial hyphae and spores. A unique feature of this device is that each microwell is surrounded by a 1.5 µm gap connected to an air-filled chamber which provide stabilized water-air interface. We used this device to observe the development of single Streptomyces spores and found that unlike those in bulk liquid culture, Streptomyces can differentiate at water-air interfaces in microscale liquid culture. Finally, we demonstrated that phenotypic A-Factor assay can be performed at defined time point of its lifecycle. This microfluidic device could become a robust tool for studying Streptomyces multi-cellular differentiation and interaction at single cell level.IMPORTANCEWe describe a microfluidic device that mimics the natural porous environment for the growth and development of Streptomyces, the model system for bacterial multicellularity. The microfluidic device is used for cultivation and the entire lifecycle observation of Streptomyces development from single-cell level, including growth of aerial filaments. The aerial hyphae development of Streptomyces at the water-air interface was observed at real time in the microfluidic device. The early growth, opportunistic transformation (in the gap), and merging of aerial hyphae of Streptomyces in the microfluidic device were observed for the first time. It will play an important role in finding single-cell heterogeneity to study secondary metabolites related to the complex lifecycle of Streptomyces.

We conducted a longitudinal study of cryptosporidiosis from birth to three years of age in an urban slum of Dhaka Bangladesh. Fecal DNA was extracted from monthly surveillance samples and diarrheal stool samples collected from 392 infants from birth to three years. A pan-Cryptosporidium qPCR assay was used to identify sub-clinical and symptomatic cryptosporidiosis. Anthropometric measurements were collected quarterly to assess child nutritional status. 31% (121/392) of children experienced a single and 57% (222/392) multiple infections with Cryptosporidium. Repeat infections had a lower burden of parasites in the stool (Cq slope = -1.85; p, Author summary Cryptosporidium is one of the top causes of diarrhea and growth faltering in Bangladesh infants. We discovered that a prior infection resulted in incomplete immunity that protected from diarrhea and growth faltering but not infection and was associated with mucosal IgA against a sporozoite surface protein Cp23. The most important implication of these findings is that a cryptosporidiosis vaccine may not need to achieve complete protection from infection to have a beneficial impact on child health.

Immune dysregulation is characteristic of the more severe stages of SARS-CoV-2 infection. Understanding the mechanisms by which the immune system contributes to COVID-19 severity may open new avenues to treatment. Here we report that elevated interleukin-13 (IL-13) was associated with the need for mechanical ventilation in two independent patient cohorts. In addition, patients who acquired COVID-19 while prescribed Dupilumab had less severe disease. In SARS-CoV-2 infected mice, IL-13 neutralization reduced death and disease severity without affecting viral load, demonstrating an immunopathogenic role for this cytokine. Following anti-IL-13 treatment in infected mice, in the lung, hyaluronan synthase 1 (Has1) was the most downregulated gene and hyaluronan accumulation was decreased. Blockade of the hyaluronan receptor, CD44, reduced mortality in infected mice, supporting the importance of hyaluronan as a pathogenic mediator, and indicating a new role for IL-13 in lung disease. Understanding the role of IL-13 and hyaluronan has important implications for therapy of COVID-19 and potentially other pulmonary diseases., Summary: IL-13 levels are elevated in patients with severe COVID-19. In a mouse model of disease, IL-13 neutralization results in reduced disease and lung hyaluronan deposition. Similarly, blockade of hyaluronan’s receptor, CD44, reduces disease, highlighting a novel mechanism for IL-13-mediated pathology.

Hospital-acquired infection is a great challenge for clinical treatment due to pathogens&rsquo, biofilm formation and their antibiotic resistance. Here, we investigate the effect of antiseptic agent polyhexamethylene biguanide (PHMB) and undecylenamidopropyl betaine (UB) against biofilms of four pathogens that are often found in hospitals, including Gram-negative bacteria Pseudomonas aeruginosa and Escherichia coli, Gram-positive bacteria Staphylococcus aureus, and pathogenic fungus, Candida albicans. We show that 0.02% PHMB, which is 10-fold lower than the concentration of commercial products, has a strong inhibitory effect on the growth, initial attachment, and biofilm formation of all tested pathogens. PHMB can also disrupt the preformed biofilms of these pathogens. In contrast, 0.1% UB exhibits a mild inhibitory effect on biofilm formation of the four pathogens. This concentration inhibits the growth of S. aureus and C. albicans yet has no growth effect on P. aeruginosa or E. coli. UB only slightly enhances the anti-biofilm efficacy of PHMB on P. aeruginosa biofilms. However, pretreatment with PslG, a glycosyl hydrolase that can efficiently inhibit and disrupt P. aeruginosa biofilm, highly enhances the clearance effect of PHMB on P. aeruginosa biofilms. Meanwhile, PslG can also disassemble the preformed biofilms of the other three pathogens within 30 min to a similar extent as UB treatment for 24 h.

Pseudomonas aeruginosa is an opportunistic pathogen that can infect a wide variety of hosts including humans, plants, and animals. The production of virulence factors is the determinant of the infection paradigm and is under orchestrated regulation via cell-to-cell communication process called quorum sensing (QS). To disable QS circuits and prevent bacterial infections, a large battery of anti-QS agents, particularly from traditional Chinese medicine have been developed. Here, we used P. aeruginosa as a model microorganism to investigate the effect of traditional Chinese medicine Tanreqing (TRQ) formula on bacterial pathogenicity. Phenotypic analysis showed that TRQ treatment could completely inhibit the production of phenazine pyocyanin and moderately inhibit the production of virulence factors such as rhamnolipids, elastase, and alkaline protease. Further transcriptomic analyses revealed that TRQ treatment could significantly attenuate the expression of QS-regulated genes in P. aeruginosa and TRQ-treated P. aeruginosa regulon shared a large overlap with QS regulon. Component contribution to QS inhibition shed light on the indispensable role of all five components in TRQ formula. Further genetic analysis indicated that upstream regulators of QS systems, including two-component systems GacS/GacA and PprA/PprB, were both inhibited by TRQ treatment. Finally, our TRQ formula could efficiently protect Caenorhabditis elegans from killing by P. aeruginosa. Altogether, we have proved TRQ formula as an effective and specific agent to attenuate bacterial virulence and combat bacterial infections.

In a cohort of infants, we found that lack of the Lewis histo-blood group antigen was associated with increased susceptibility to shigellosis. Broadly inhibiting fucosylation in epithelial cells in vitro decreased invasion by Shigella flexneri. These results support a role for fucosylated glycans in susceptibility to shigellosis.

Microsporidia are a large group of unicellular parasites that infect insects and mammals. The simpler life cycle of microsporidia in insects provides a model system for understanding their evolution and molecular interactions with their hosts. However, no complete genome is available for insect-parasitic microsporidian species. The complete genome ofAntonospora locustae, a microsporidian parasite that obligately infects insects, is reported here. The genome size ofA. locustaeis 3 170 203 nucleotides, composed of 17 chromosomes onto which a total of 1857 annotated genes have been mapped and detailed. A unique feature of theA. locustaegenome is the presence of an ultra-low GC region of approximately 25 kb on 16 of the 17 chromosomes, in which the average GC content is only 20 %. Transcription profiling indicated that the ultra-low GC region of the parasite could be associated with differential regulation of host defences in the fat body to promote the parasite’s survival and propagation. Phylogenetic gene analysis showed thatA. locustae, and the microsporidian family in general, is likely at an evolutionarily transitional position between prokaryotes and eukaryotes, and that it evolved independently. Transcriptomic analysis showed thatA. locustaecan systematically inhibit the locust phenoloxidase PPO, TCA and glyoxylate cycles, and PPAR pathways to escape melanization, and can activate host energy transfer pathways to support its reproduction in the fat body, which is an insect energy-producing organ. Our study provides a platform and model for studies of the molecular mechanisms of microsporidium–host interactions in an energy-producing organ and for understanding the evolution of microsporidia.

Clostridioides difficile infection is the most common health care-associated infection in the United States with more than 20% patients experiencing symptomatic recurrence. The complex nature of host-bacterium interactions makes it difficult to predict the course of the disease based solely on clinical parameters. In the present study, we built a robust prediction model using representative plasma biomarkers and clinical parameters for 90-day all-cause mortality. Risk prediction based on immune biomarkers and clinical variables may contribute to treatment selection for patients as well as provide insight into the role of immune system in C. difficile pathogenesis., There is a pressing need for biomarker-based models to predict mortality from and recurrence of Clostridioides difficile infection (CDI). Risk stratification would enable targeted interventions such as fecal microbiota transplant, antitoxin antibodies, and colectomy for those at highest risk. Because severity of CDI is associated with the immune response, we immune profiled patients at the time of diagnosis. The levels of 17 cytokines in plasma were measured in 341 CDI inpatients. The primary outcome of interest was 90-day mortality. Increased tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), C-C motif chemokine ligand 5 (CCL-5), suppression of tumorigenicity 2 receptor (sST-2), IL-8, and IL-15 predicted mortality by univariate analysis. After adjusting for demographics and clinical characteristics, the mortality risk (as indicated by the hazard ratio [HR]) was higher for patients in the top 25th percentile for TNF-α (HR = 8.35, P = 0.005) and IL-8 (HR = 4.45, P = 0.01) and lower for CCL-5 (HR = 0.18, P ≤ 0.008). A logistic regression risk prediction model was developed and had an area under the receiver operating characteristic curve (AUC) of 0.91 for 90-day mortality and 0.77 for 90-day recurrence. While limited by being single site and retrospective, our work resulted in a model with a substantially greater predictive ability than white blood cell count. In conclusion, immune profiling demonstrated differences between patients in their response to CDI, offering the promise for precision medicine individualized treatment.

Elevated mammalian target of rapamycin (mTOR) signaling has been reported to correlate with poor prognosis in acute lymphoblastic leukemia (ALL) patients. Rapamycin, an mTOR kinase inhibitor, and also a potent autophagy inducer, could not only effectively reverse glucocorticoid resistance, but also promote autophagy in the ALL cells. Autophagy has been suggested to play a paradoxical role in cancer treatment. The aim of this study was to address the role of the rapamycin-induced autophagy in the leukemia treatment. Materials and methods Cell proliferation was detected by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay in ALL cell lines of CEM-C1 and CEM-C7. Western Blot analysis was performed to test protein expressions. Results Inhibition of mTOR by rapamycin could reverse glucocorticoid resistance in CEM-C1 cells, and also induce autophagy in these cells by up-regulation of LC3-II and Beclin-1 expressions. This autophagy played a pro-survival role since its inhibition by 6-amino-3-methylpurine or chroloquine could enhance rapamycin-induced cell death. Rapamycin increased the expression of intracellular ferritin, and this effect could be totally blocked by 6-amino-3-methylpurine and chroloquine, suggesting that the protective role of autophagy might be mediated through up-regulation of ferritin, the major iron-binding stress protein. Ciclopirox olamine, an iron chelator, could enhance rapamycin's anti-leukemia effect by down-regulation of intracellular ferritin expression. Conclusions All these findings would suggest that rapamycin-induced autophagy plays a pro-survival role in leukemia cells and this effect might be mediated by up-regulation of intracellular ferritin expression. We hypothesize that the combination of mTOR pathway inhibitors and autophagy inhibition is rational and would induce strong anti-leukemia effects in ALL.

Pseudomonas aeruginosa is an environmental microorganism that can thrive in diverse ecological niches including plants, animals, water, soil, and crude oil. It also one of the microorganism widely used in tertiary recovery of crude oil and bioremediation. However, the genomic information regarding the mechanisms of survival and adapation of this bacterium in crude oil is still limited. In this study, three Pseudomonads strains (named as IMP66, IMP67, and IMP68) isolated from crude oil were taken for whole-genome sequencing by using a hybridized PacBio and Illumina approach. The phylogeny analysis showed that the three strains were all P. aeruginosa species and clustered in clade 1, the group with PAO1 as a representitive. Subsequent comparative genomic analysis revealed a high degree of individual genomic plasticity, with a probable alkane degradation genomic island, one type I-F CRISPR-Cas system and several prophages integrated into their genomes. Nine genes encoding alkane hydroxylases (AHs) homologs were found in each strain, which might enable these strains to degrade alkane in crude oil. P. aeruginosa can produce rhamnolipids (RLs) biosurfactant to emulsify oil, which enables their survival in crude oil enviroments. Our previous report showed that IMP67 and IMP68 were high RLs producers, while IMP66 produced little RLs. Genomic analysis suggested that their RLs yield was not likely due to differences at genetic level. We then further analyzed the quorum sensing (QS) signal molecules that regulate RLs synthesis. IMP67 and IMP68 produced more N-acyl-homoserine lactones (AHLs) signal molecules than that of PAO1 and IMP66, which could explain their high RLs yield. This study provides evidence for adaptation of P. aeruginosa in crude oil and proposes the potential application of IMP67 and IMP68 in microbial-enhanced oil recovery and bioremediation. Published version

Ruminants are critical as prey in transferring solar energy fixed by plants into carnivorous species, yet the genetic signature of the driving forces leading to the evolutionary success of the huge number of ruminant species remains largely unknown. Here we report a complete DNA map of the major histocompatibility complex (MHC) of the addax (Addax nasomaculatus) genome by sequencing a total of 47 overlapping BAC clones previously mapped to cover the MHC region. The addax MHC is composed of 3,224,151 nucleotides, harboring a total of 150 coding genes, 50 tRNA genes, and 14 non-coding RNA genes. The organization of addax MHC was found to be highly conserved to those of sheep and cattle, highlighted by a large piece of chromosome inversion that divided the MHC class II into IIa and IIb subregions. It is now highly possible that all of the ruminant species in the family of Bovidae carry the same chromosome inversion in the MHC region, inherited from a common ancestor of ruminants. Phylogenetic analysis indicated that DY, a ruminant-specific gene located at the boundary of the inversion and highly expressed in dendritic cells, was possibly evolved from DQ, with an estimated divergence time ~140 million years ago. Homology modeling showed that the overall predicted structure of addax DY was similar to that of HLA-DQ2. However, the pocket properties of P1, P4, P6, and P9, which were critical for antigen binding in the addax DY, showed certain distinctive features. Structural analysis suggested that the populations of peptide antigens presented by addax DY and HLA-DQ2 were quite diverse, which in theory could serve to promote microbial regulation in the rumen by ruminant species, contributing to enhanced grass utilization ability. In summary, the results of our study helped to enhance our understanding of the MHC evolution and provided additional supportive evidence to our previous hypothesis that an ancient chromosome inversion in the MHC region of the last common ancestor of ruminants may have contributed to the evolutionary success of current ruminants on our planet.

Opportunistic pathogen Pseudomonas aeruginosa can cause acute and chronic infections in humans. It is notorious for its resistance to antibiotics due to the formation of biofilms. Cyclic‐di‐GMP is a bacterial second messenger that plays important roles during biofilm development. There are 40 genes in P. aeruginosa predicted to participate in c‐di‐GMP biosynthesis or degradation. It is time‐consuming for the functional characterization of these genes. Here, we cloned 16 genes from P. aeruginosa PAO1 that are predicted to encode diguanylate cyclases (DGCs, responsible for c‐di‐GMP biosynthesis) and constructed their corresponding in‐frame deletion mutants. We evaluated the methods to measure the intracellular c‐di‐GMP concentration by using deletion mutants and PAO1 strains containing a plasmid expressing one of the 16 genes, respectively. Functional outputs of all PAO1‐derived stains were also detected and evaluated, including biofilm formation, production of exopolysaccharide, swimming and swarming motilities. Our data showed that measuring the c‐di‐GMP level only characterized a few DGC by using either pCdrA::gfp as a reporter or LC/MS/MS. Functional output results indicated that overexpression of a DGC gave more pronounced phenotypes than the corresponding deletion mutant and suggested that the swimming motility assay could be a quick way to briefly estimate a predicted DGC for further studies. The overall evaluation suggested 15 out of 16 predicted DGCs were functional DGCs, wherein six were characterized to encode DGCs previously. Altogether, we have provided not only a cloning library of 16 DGC‐encoding genes and their corresponding in‐frame deletion mutants but also paved ways to briefly characterize a predicted DGC., Cyclic‐di‐GMP is a bacterial second messenger that plays important roles during biofilm development. In this study, we have used Pseudomonas aeruginosa PAO1 as a model microorganism to evaluate the methods and functional outputs of 16 putative DGC‐encoding genes that are responsible for the synthesis of c‐di‐GMP. Our results have shown that the overexpression of a DGC overall has given more distinctive phenotype than the corresponding deletion mutant although multiple methods are still necessary for DGC characterization.

Biofilm bacteria are embedded within a self-secreted extracellular matrix that contains a considerable amount of proteins including many extracellular enzymes. However, little is known about the roles of such enzymes in biofilm development. Here, we studied Pseudomonas aeruginosa aminopeptidase (PaAP, encoded by PA2939 that we named the gene as paaP in this study), a quorum-sensing-regulated enzyme and one of the most abundant extracellular proteins in the biofilm matrix of this opportunistic pathogen and environmental bacterium. We found that deletion of paaP in P. aeruginosa increased initial attachment and biofilm formation at early stages of biofilm development. After 24 h growth, loss of PaAP resulted in substantial cell death and biofilm disruption. Bacterial cell death was independent of biofilm matrix polysaccharide Psl, while biofilm disruption was due to the degradation of Psl matrix by dead-bacteria-released glycosyl hydrolase PslG, thereby leading to biofilm dispersion. PaAP functioned extracellularly and aminopeptidase catalytic activity was essential for its effect on biofilm development. Our data reveal an important role of extracellular aminopeptidase in biofilm development, suggesting PaAP as a therapeutic target for preventing P. aeruginosa infection and combating biofilm-related complications.

In late 2011, a variant pseudorabies virus (vPRV) emerged in Bartha-K61-vaccinated pig herds, resulting in high morbidity and mortality of piglets in China. Since 2013, the autopsy lesions, histological examinations, virus isolation, phylogenetic analysis and selection pressure analysis of the gE gene of vPRV were recorded for 395 clinical cases, and 5,033 pig serum samples were detected by PRV gE-coated enzyme-linked immunosorbent assay. The major clinical symptoms were abortion in pregnant sows, fatal neurological signs in piglets and respiratory disease in growing pigs. Necrotic splenitis, hepatitis and lymphadenitis, haemorrhagic nephritis and non-suppurative encephalitis were observed by histopathological examination. Typical eosinophilic inclusion bodies were found in the nuclei of liver cells. Using PCR, 110 samples among 395 clinical cases tested positive for the gE gene. Fifteen vPRV strains were isolated and confirmed by sequencing and phylogenetic analysis of the gE gene. The strains shared 97.1%-99.9% nucleotide (nt) and 96.6%-99.5% amino acid (aa) homology with PRV reference strains. Selection pressure analysis showed that one site in the codons of glycoprotein E was under positive selection. Of the 5,033 serum samples, 2,909 were positive by ELISA for a positive rate of 57.8%. These results showed that vPRV was still prevalent in Shandong Province, indicating severe PRV infectious pressure. The preparation of new vaccines against PRV is extremely urgent.

To better understand the evolution of hepadnaviruses, we sampled bats from Guizhou, Henan and Zhejiang provinces, China, and rodents from Zhejiang province. Genetically diverse hepadnaviruses were identified in a broad range of bat species, with an overall prevalence of 13.3%. In contrast, no rodent hepadnaviruses were identified. The newly discovered bat hepadnaviruses fell into two distinct phylogenetic groups. The viruses within the first group exhibited high diversity, with some closely related to viruses previously identified in Yunnan province. Strikingly, the newly discovered viruses sampled from Jiyuan city in the second phylogenetic group were most closely related to those found in bats from West Africa, suggestive of a long-term association between bats and hepadnaviruses. A co-phylogenetic analysis revealed frequent cross-species transmission among bats from different species, genera, and families. Overall, these data suggest that there are likely few barriers to the cross-species transmission of bat hepadnaviruses., Highlights • Diverse hepadnaviruses are identified in a broad range of bat species in China. • Some of them were closely related to those previously identified in China. • The viruses from Jiyuan were most closely related to Gabon bat hepadnaviruses. • Newly discovered viruses did not clustered by bat species or geographic location. • Frequent cross-species transmission among different bat species was observed.

INTRODUCTION: Cigarette smoking is one of the largest causes of preventable death worldwide. This study aimed to identify susceptibility loci for age at smoking initiation (ASI) by performing an exome-wide association analysis. METHODS: A total of 2510 smokers of either African-American (AA) or European-American (EA) origin were genotyped and analyzed at both the single nucleotide polymorphism (SNP) and gene levels. After removal of those SNPs with a minor allele frequency (

Previous studies have shown that four and a half LIM domain protein 2 (FHL2) plays an essential role in the regulation of follicular development in mammals. Although the FHL2 genes of human and mouse have been well characterized, the expression and location of FHL2 in ovary and the biological functions of FHL2 on granulosa cells (GCs) of ovine are still not clear. In this study, full-length complementary DNA (cDNA) of FHL2 from ovine follicular GCs was amplified by real-time PCR (RT-PCR). The expression and location of FHL2 in ovary and GCs of ovine were studied by immunohistochemistry and immunofluorescence, and the biological effects of FHL2 on the cell proliferation, cell apoptosis, cell cycles and expression level of related genes of ovine GCs were also explored by overexpression or knockdown of FHL2. The results indicated that FHL2 was expressed in ovine follicular GCs and the sequence of the FHL2 cDNA was consistent with that predicted in GenBank, which did not cause an amino acid change. According to the results, FHL2 was expressed in ovine ovary and mainly located in the cytoplasm and nucleus of GCs. In addition, overexpression of FHL2 significantly reduced the cell viability, promoted the cell apoptosis and decreased the percentage of G0/G1 and S phase cells. RT-PCR showed that overexpression of FHL2 significantly increased the mRNA expression level of Bax and decreased the expression of Bcl-2 and the Bcl-2/Bax mRNA ratio compared with the control group. Besides, the knockdown of FHL2 gene in ovine GCs significantly improved the cell viability, suppressed the cell apoptosis, decreased the mRNA expression level of Caspase-3 gene, increased the Bcl-2/Bax mRNA ratio and increased the percentage of S and G2/M phase cells. Our results suggest that FHL2 may play an important role in the biological functions of GCs in ovine.

MC3 Working Group: Rehan Akbani21, Pavana Anur22, Matthew H. Bailey1,2,3, Alex Buchanan9, Kami Chiotti9, Kyle Covington12,23, Allison Creason9, Li Ding1,2,3,20, Kyle Ellrott9, Yu Fan21, Steven Foltz1,2, Gad Getz8,14,15,16, Walker Hale12, David Haussler24,25, Julian M. Hess8,26, Carolyn M. Hutter27, Cyriac Kandoth28, Katayoon Kasaian29,30, Melpomeni Kasapi27, Dave Larson1 , Ignaty Leshchiner8, John Letaw31, Singer Ma32, Michael D. McLellan1,3,20, Yifei Men32, Gordon B. Mills33,34, Beifang Niu35, Myron Peto22, Amie Radenbaugh24, Sheila M. Reynolds36, Gordon Saksena8, Heidi Sofia27, Chip Stewart8, Adam J. Struck31, Joshua M. Stuart24,37, Wenyi Wang21, John N. Weinstein38, David A. Wheeler12,13, Christopher K. Wong24,39, Liu Xi12 & Kai Ye40,41 21Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 22Molecular and Medical Genetics, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 23Castle Biosciences Inc, Friendswood, TX 77546, USA. 24UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 25Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 26Massachusetts General Hospital Center for Cancer Research, Charlestown, MA 02114, USA. 27National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20894, USA. 28Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. 29Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada. 30Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada. 31Computational Biology Program, School of Medicine, Oregon Health and Science University, Portland, OR 97239, USA. 32DNAnexus Inc, Mountain View, CA 94040, USA. 33Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA. 34Precision Oncology, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 35Computer Network Information Center, Chinese Academy of Sciences, Beijing, China. 36Institute for Systems Biology, Seattle, WA 98109, USA. 37Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 38Department of Bioinformatics and Computational Biology and Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 39Biomolecular Engineering Department, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 40School of Elect, PCAWG novel somatic mutation calling methods working group: Matthew H. Bailey1,2,3, Beifang Niu35, Matthias Bieg42,43, Paul C. Boutros6,44,45,46, Ivo Buchhalter43,47,48, Adam P. Butler49, Ken Chen50, Zechen Chong51, Li Ding1,2,3,20, Oliver Drechsel52,53, Lewis Jonathan Dursi6,7, Roland Eils47,48,54,55, Kyle Ellrott9, Shadrielle M. G. Espiritu6, Yu Fan21, Robert S. Fulton1,3,20, Shengjie Gao56, Josep L. l. Gelpi57,58, Mark B. Gerstein5,18,19, Gad Getz8,14,15,16, Santiago Gonzalez59,60, Ivo G. Gut52,61, Faraz Hach62,63, Michael C. Heinold47,48, Julian M. Hess8,26, Jonathan Hinton49, Taobo Hu64, Vincent Huang6, Yi Huang65,66, Barbara Hutter43,67,68, David R. Jones49, Jongsun Jung69, Natalie Jäger47, Hyung-Lae Kim70, Kortine Kleinheinz47,48, Sushant Kumar5,19, Yogesh Kumar64, Christopher M. Lalansingh6, Ignaty Leshchiner8, Ivica Letunic71, Dimitri Livitz8, Eric Z. Ma64, Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Michael D. McLellan1,3,20, Andrew Menzies49, Ana Milovanovic57, Morten Muhlig Nielsen73, Stephan Ossowski52,53,74, Nagarajan Paramasivam43,47, Jakob Skou Pedersen73,75, Marc D. Perry76,77, Montserrat Puiggròs57, Keiran M. Raine49, Esther Rheinbay8,14,72, Romina Royo57, S. Cenk Sahinalp62,78,79, Gordon Saksena8, Iman Sarrafi62,78, Matthias Schlesner47,80, Jared T. Simpson6,17, Lucy Stebbings49, Chip Stewart8, Miranda D. Stobbe52,61, Jon W. Teague49, Grace Tiao8, David Torrents57,81, Jeremiah A. Wala8,14,82, Jiayin Wang1,40,66, Wenyi Wang21, Sebastian M. Waszak60, Joachim Weischenfeldt60,83,84, Michael C. Wendl1,10,11, Johannes Werner47,85, Zhenggang Wu64, Hong Xue64, Sergei Yakneen60, Takafumi N. Yamaguchi6, Kai Ye40,41, Venkata D. Yellapantula20,86, Christina K. Yung76 & Junjun Zhang76, PCAWG Consortium: Lauri A. Aaltonen87, Federico Abascal49, Adam Abeshouse88, Hiroyuki Aburatani89, David J. Adams49, Nishant Agrawal90, Keun Soo Ahn91, Sung-Min Ahn92, Hiroshi Aikata93, Rehan Akbani21, Kadir C. Akdemir50, Hikmat Al-Ahmadie88, Sultan T. Al-Sedairy94, Fatima Al-Shahrour95, Malik Alawi96,97, Monique Albert98, Kenneth Aldape99,100, Ludmil B. Alexandrov49,101,102, Adrian Ally30, Kathryn Alsop103, Eva G. Alvarez104,105,106, Fernanda Amary107, Samirkumar B. Amin108,109,110, Brice Aminou76, Ole Ammerpohl111,112, Matthew J. Anderson113, Yeng Ang114, Davide Antonello115, Pavana Anur22, Samuel Aparicio116, Elizabeth L. Appelbaum1,117, Yasuhito Arai118, Axel Aretz119, Koji Arihiro93, Shun-ichi Ariizumi120, Joshua Armenia121, Laurent Arnould122, Sylvia Asa123,124, Yassen Assenov125, Gurnit Atwal6,126,127, Sietse Aukema112,128, J. Todd Auman129, Miriam R. Aure130, Philip Awadalla6,126, Marta Aymerich131, Gary D. Bader126, Adrian Baez-Ortega132, Matthew H. Bailey1,2,3, Peter J. Bailey133, Miruna Balasundaram30, Saianand Balu134, Pratiti Bandopadhayay8,135,136, Rosamonde E. Banks137, Stefano Barbi138, Andrew P. Barbour139,140, Jonathan Barenboim6, Jill Barnholtz-Sloan141,142, Hugh Barr143, Elisabet Barrera59, John Bartlett98,144, Javier Bartolome57, Claudio Bassi115, Oliver F. Bathe145,146, Daniel Baumhoer147, Prashant Bavi148, Stephen B. Baylin149,150, Wojciech Bazant59, Duncan Beardsmore151, Timothy A. Beck152,153, Sam Behjati49, Andreas Behren154, Beifang Niu35, Cindy Bell155, Sergi Beltran52,61, Christopher Benz156, Andrew Berchuck157, Anke K. Bergmann158, Erik N. Bergstrom101,102, Benjamin P. Berman159,160,161, Daniel M. Berney162, Stephan H. Bernhart163,164,165, Rameen Beroukhim8,14,82, Mario Berrios166, Samantha Bersani167, Johanna Bertl73,168, Miguel Betancourt169, Vinayak Bhandari6,44, Shriram G. Bhosle49, Andrew V. Biankin133,170,171,172, Matthias Bieg42,43, Darell Bigner173, Hans Binder163,164, Ewan Birney59, Michael Birrer72, Nidhan K. Biswas174, Bodil Bjerkehagen147,175, Tom Bodenheimer134, Lori Boice176, Giada Bonizzato177, Johann S. De Bono178, Arnoud Boot179,180, Moiz S. Bootwalla166, Ake Borg181, Arndt Borkhardt182, Keith A. Boroevich183,184, Ivan Borozan6, Christoph Borst185, Marcus Bosenberg186, Mattia Bosio52,53,57, Jacqueline Boultwood187, Guillaume Bourque188,189, Paul C. Boutros6,44,45,46, G. Steven Bova190, David T. Bowen49,191, Reanne Bowlby30, David D. L. Bowtell103, Sandrine Boyault192, Rich Boyce59, Jeffrey Boyd193, Alvis Brazma59, Paul Brennan194, Daniel S. Brewer195,196, Arie B. Brinkman197, Robert G. Bristow44,198,199,200,201, Russell R. Broaddus99, Jane E. Brock202, Malcolm Brock203, Annegien Broeks204, Angela N. Brooks8,24,37,82, Denise Brooks30, Benedikt Brors67,205,206, Søren Brunak207,208, Timothy J. C. Bruxner113,209, Alicia L. Bruzos104,105,106, Alex Buchanan9, Ivo Buchhalter43,47,48, Christiane Buchholz210, Susan Bullman8,82, Hazel Burke211, Birgit Burkhardt212, Kathleen H. Burns213,214, John Busanovich8,215, Carlos D. Bustamante216,217, Adam P. Butler49, Atul J. Butte218, Niall J. Byrne76, Anne-Lise Børresen-Dale130,219, Samantha J. Caesar-Johnson220, Andy Cafferkey59, Declan Cahill221, Claudia Calabrese59,60, Carlos Caldas222,223, Fabien Calvo224, Niedzica Camacho178, Peter J. Campbell49,225, Elias Campo226,227, Cinzia Cantù177, Shaolong Cao21, Thomas E. Carey228, Joana Carlevaro-Fita229,230,231, Rebecca Carlsen30, Ivana Cataldo167,177, Mario Cazzola232, Jonathan Cebon154, Robert Cerfolio233, Dianne E. Chadwick234, Dimple Chakravarty235, Don Chalmers236, Calvin Wing Yiu Chan47,237, Kin Chan238, Michelle Chan-Seng-Yue148, Vishal S. Chandan239, David K. Chang133,170, Stephen J. Chanock240, Lorraine A. Chantrill170,241, Aurélien Chateigner76,242, Nilanjan Chatterjee149,243, Kazuaki Chayama93, Hsiao-Wei Chen114,121, Jieming Chen218, Ken Chen50, Yiwen Chen21, Zhaohong Chen244, Andrew D. Cherniack8,82, Jeremy Chien245, Yoke-Eng Chiew246,247, Suet-Feung Chin222,223, Juok Cho8, Sunghoon Cho248, Jung Kyoon Choi249, Wan Choi250, Christine Chomienne251, Zechen Chong51, Su Pin Choo252, Angela Chou170,246, Angelika N. Christ113, Elizabeth L. Christie103, Eric Chuah30, Carrie Cibulskis8, Kristian Cibulskis8, Sara Cingarlini253, Peter Clapham49, Alexander Claviez254, Sean Cleary148,255, Nicole Cloonan256, Marek Cmero257,258,259, Colin C. Collins62, Ashton A. Connor255,260, Susanna L. Cooke133, Colin S. Cooper178,196,261, Leslie Cope149, Vincenzo Corbo138,177, Matthew G. Cordes1,262, Stephen M. Cordner263, Isidro Cortés-Ciriano264,265,266, Kyle Covington12,23, Prue A. Cowin267, Brian Craft24, David Craft8,268, Chad J. Creighton269, Yupeng Cun270, Erin Curley271, Ioana Cutcutache179,180, Karolina Czajka272, Bogdan Czerniak99,273, Rebecca A. Dagg274, Ludmila Danilova149, Maria Vittoria Davi275, Natalie R. Davidson276,277,278,279,280, Helen Davies49,281,282, Ian J. Davis283, Brandi N. Davis-Dusenbery284, Kevin J. Dawson49, Francisco M. De La Vega216,217,285, Ricardo De Paoli-Iseppi211, Timothy Defreitas8, Angelo P. Dei Tos286, Olivier Delaneau287,288,289, John A. Demchok220, Jonas Demeulemeester290,291, German M. Demidov52,53,74, Deniz Demircioğlu292,293, Nening M. Dennis221, Robert E. Denroche148, Stefan C. Dentro49,290,294, Nikita Desai76, Vikram Deshpande72, Amit G. Deshwar295, Christine Desmedt296,297, Jordi Deu-Pons298,299, Noreen Dhalla30, Neesha C. Dhani300, Priyanka Dhingra301,302, Rajiv Dhir303, Anthony DiBiase304, Klev Diamanti305, Li Ding1,2,3,20, Shuai Ding306, Huy Q. Dinh159, Luc Dirix307, HarshaVardhan Doddapaneni12, Nilgun Donmez62,78, Michelle T. Dow244, Ronny Drapkin308, Oliver Drechsel52,53, Ruben M. Drews223, Serge Serge49, Tim Dudderidge150,221, Ana Dueso-Barroso57, Andrew J. Dunford8, Michael Dunn309, Lewis Jonathan Dursi6,7, Fraser R. Duthie133,310, Ken Dutton-Regester311, Jenna Eagles272, Douglas F. Easton312,313, Stuart Edmonds314, Paul A. Edwards223,315, Sandra E. Edwards178, Rosalind A. Eeles178,221, Anna Ehinger316, Juergen Eils54,55, Roland Eils47,48,54,55, Adel El-Naggar99,273, Matthew Eldridge223, Kyle Ellrott9, Serap Erkek60, Georgia Escaramis53,317,318, Shadrielle M. G. Espiritu6, Xavier Estivill53,319, Dariush Etemadmoghadam103, Jorunn E. Eyfjord320, Bishoy M. Faltas280, Daiming Fan321, Yu Fan21, William C. Faquin72, Claudiu Farcas244, Matteo Fassan322, Aquila Fatima323, Francesco Favero324, Nodirjon Fayzullaev76, Ina Felau220, Sian Fereday103, Martin L. Ferguson325, Vincent Ferretti76,326, Lars Feuerbach205, Matthew A. Field327, J. Lynn Fink57,113, Gaetano Finocchiaro328, Cyril Fisher221, Matthew W. Fittall290, Anna Fitzgerald329, Rebecca C. Fitzgerald282, Adrienne M. Flanagan330, Neil E. Fleshner331, Paul Flicek59, John A. Foekens332, Kwun M. Fong333, Nuno A. Fonseca59,334, Christopher S. Foster335,336, Natalie S. Fox6, Michael Fraser6, Scott Frazer8, Milana Frenkel-Morgenstern337, William Friedman338, Joan Frigola298, Catrina C. Fronick1,262, Akihiro Fujimoto184, Masashi Fujita184, Masashi Fukayama339, Lucinda A. Fulton1 , Robert S. Fulton1,3,20, Mayuko Furuta184, P. Andrew Futreal340, Anja Füllgrabe59, Stacey B. Gabriel8, Steven Gallinger148,255,260, Carlo Gambacorti-Passerini341, Jianjiong Gao121, Shengjie Gao56, Levi Garraway82, Øystein Garred342, Erik Garrison49, Dale W. Garsed103, Nils Gehlenborg8,343, Josep L. l. Gelpi57,58, Joshy George110, Daniela S. Gerhard344, Clarissa Gerhauser345, Jeffrey E. Gershenwald346,347, Mark B. Gerstein5,18,19, Moritz Gerstung59,60, Gad Getz8,14,15,16, Mohammed Ghori49, Ronald Ghossein348, Nasra H. Giama349, Richard A. Gibbs12, Anthony J. Gill170,350, Pelvender Gill351, Dilip D. Giri348, Dominik Glodzik49, Vincent J. Gnanapragasam352,353, Maria Elisabeth Goebler354, Mary J. Goldman24, Carmen Gomez355, Santiago Gonzalez59,60, Abel Gonzalez-Perez298,299,356, Dmitry A. Gordenin357, James Gossage358, Kunihito Gotoh359, Ramaswamy Govindan3, Dorthe Grabau360, Janet S. Graham133,361, Robert C. Grant148,260, Anthony R. Green315, Eric Green27, Liliana Greger59, Nicola Grehan282, Sonia Grimaldi177, Sean M. Grimmond362, Robert L. Grossman363, Adam Grundhoff97,364, Gunes Gundem88, Qianyun Guo75, Manaswi Gupta8, Shailja Gupta365, Ivo G. Gut52,61, Marta Gut52,61, Jonathan Göke292,366, Gavin Ha8, Andrea Haake111, David Haan37, Siegfried Haas185, Kerstin Haase290, James E. Haber367, Nina Habermann60, Faraz Hach62,63, Syed Haider6, Natsuko Hama118, Freddie C. Hamdy351, Anne Hamilton267, Mark P. Hamilton368, Leng Han369, George B. Hanna370, Martin Hansmann371, Nicholas J. Haradhvala8,72, Olivier Harismendy102,372, Ivon Harliwong113, Arif O. Harmanci5,373, Eoghan Harrington374, Takanori Hasegawa375, David Haussler24,25, Steve Hawkins223, Shinya Hayami376, Shuto Hayashi375, D. Neil Hayes134,377,378, Stephen J. Hayes379,380, Nicholas K. Hayward211,311, Steven Hazell221, Yao He381, Allison P. Heath382, Simon C. Heath52,61, David Hedley300, Apurva M. Hegde38, David I. Heiman8, Michael C. Heinold47,48, Zachary Heins88, Lawrence E. Heisler152, Eva Hellstrom-Lindberg383, Mohamed Helmy384, Seong Gu Heo385, Austin J. Hepperla134, José María Heredia-Genestar386, Carl Herrmann47,48,387, Peter Hersey211, Julian M. Hess8,26, Holmfridur Hilmarsdottir320, Jonathan Hinton49, Satoshi Hirano388, Nobuyoshi Hiraoka389, Katherine A. Hoadley134,390, Asger Hobolth75,168, Ermin Hodzic78, Jessica I. Hoell182, Steve Hoffmann163,164,165,391, Oliver Hofmann392, Andrea Holbrook166, Aliaksei Z. Holik53, Michael A. Hollingsworth393, Oliver Holmes209,311, Robert A. Holt30, Chen Hong205,237, Eun Pyo Hong385, Jongwhi H. Hong394, Gerrit K. Hooijer395, Henrik Hornshøj73, Fumie Hosoda118, Yong Hou56,396, Volker Hovestadt397, William Howat352, Alan P. Hoyle134, Ralph H. Hruban149, Jianhong Hu12, Taobo Hu64, Xing Hua240, Kuan-lin Huang1,398, Mei Huang176, Mi Ni Huang179,180, Vincent Huang6, Yi Huang65,66, Wolfgang Huber60, Thomas J. Hudson272,399, Michael Hummel400, Jillian A. Hung246,247, David Huntsman401, Ted R. Hupp402, Jason Huse88, Matthew R. Huska403, Barbara Hutter43,67,68, Carolyn M. Hutter27, Daniel Hübschmann48,54,404,405,406, Christine A. Iacobuzio-Donahue348, Charles David Imbusch205, Marcin Imielinski407,408, Seiya Imoto375, William B. Isaacs409, Keren Isaev6,44, Shumpei Ishikawa410, Murat Iskar397, S. M. Ashiqul Islam244, Michael Ittmann411,412,413, Sinisa Ivkovic284, Jose M. G. Izarzugaza414, Jocelyne Jacquemier415, Valerie Jakrot211, Nigel B. Jamieson133,172,416, Gun Ho Jang148, Se Jin Jang417, Joy C. Jayaseelan12, Reyka Jayasinghe1 , Stuart R. Jefferys134, Karine Jegalian418, Jennifer L. Jennings419, Seung-Hyup Jeon250, Lara Jerman60,420, Yuan Ji421,422, Wei Jiao6, Peter A. Johansson311, Amber L. Johns170, Jeremy Johns272, Rory Johnson230,423, Todd A. Johnson183, Clemency Jolly290, Yann Joly424, Jon G. Jonasson320, Corbin D. Jones425, David R. Jones49, David T. W. Jones426,427, Nic Jones428, Steven J. M. Jones30, Jos Jonkers204, Young Seok Ju49,249, Hartmut Juhl429, Jongsun Jung69, Malene Juul73, Randi Istrup Juul73, Sissel Juul374, Natalie Jäger47, Rolf Kabbe47, Andre Kahles276,277,278,279,430, Abdullah Kahraman431,432,433, Vera B. Kaiser434, Hojabr Kakavand211, Sangeetha Kalimuthu148, Christof von Kalle405, Koo Jeong Kang91, Katalin Karaszi351, Beth Karlan435, Rosa Karlić436, Dennis Karsch437, Katayoon Kasaian29,30, Karin S. Kassahn113,438, Hitoshi Katai439, Mamoru Kato440, Hiroto Katoh410, Yoshiiku Kawakami93, Jonathan D. Kay117, Stephen H. Kazakoff209,311, Marat D. Kazanov441,442,443, Maria Keays59, Electron Kebebew444,445, Richard F. Kefford446, Manolis Kellis8,447, James G. Kench170,350,448, Catherine J. Kennedy246,247, Jules N. A. Kerssemakers47, David Khoo273, Vincent Khoo221, Narong Khuntikeo115,449, Ekta Khurana301,302,450,451, Helena Kilpinen117, Hark Kyun Kim452, Hyung-Lae Kim70, Hyung-Yong Kim415, Hyunghwan Kim250, Jaegil Kim8, Jihoon Kim453, Jong K. Kim454, Youngwook Kim455,456, Tari A. King457,458,459, Wolfram Klapper128, Kortine Kleinheinz47,48, Leszek J. Klimczak460, Stian Knappskog49,461, Michael Kneba437, Bartha M. Knoppers424, Youngil Koh462,463, Jan Komorowski305,464, Daisuke Komura410, Mitsuhiro Komura375, Gu Kong415, Marcel Kool426,465, Jan O. Korbel59,60, Viktoriya Korchina12, Andrey Korshunov465, Michael Koscher465, Roelof Koster466, Zsofia Kote-Jarai178, Antonios Koures244, Milena Kovacevic284, Barbara Kremeyer49, Helene Kretzmer164,165, Markus Kreuz467, Savitri Krishnamurthy99,468, Dieter Kube469, Kiran Kumar8, Pardeep Kumar221, Sushant Kumar5,19, Yogesh Kumar64, Ritika Kundra114,121, Kirsten Kübler8,14,72, Ralf Küppers470, Jesper Lagergren383,471, Phillip H. Lai166, Peter W. Laird472, Sunil R. Lakhani473, Christopher M. Lalansingh6, Emilie Lalonde6, Fabien C. Lamaze6, Adam Lambert351, Eric Lander8, Pablo Landgraf474,475, Luca Landoni115, Anita Langerød130, Andrés Lanzós230,231,423, Denis Larsimont476, Erik Larsson477, Mark Lathrop189, Loretta M. S. Lau478, Chris Lawerenz55, Rita T. Lawlor177, Michael S. Lawrence8,72,183, Alexander J. Lazar99,108, Xuan Le479, Darlene Lee30, Donghoon Lee5, Eunjung Alice Lee480, Hee Jin Lee417, Jake June-Koo Lee264,266, Jeong-Yeon Lee481, Juhee Lee482, Ming Ta Michael Lee340, Henry Lee-Six49, Kjong-Van Lehmann276,277,278,279,430, Hans Lehrach483, Dido Lenze400, Conrad R. Leonard209,311, Daniel A. Leongamornlert49,178, Ignaty Leshchiner8, Louis Letourneau484, Ivica Letunic71, Douglas A. Levine88,485, Lora Lewis12, Tim Ley486, Chang Li56,396, Constance H. Li6,44, Haiyan Irene Li30, Jun Li21, Lin Li56, Shantao Li5, Siliang Li56,396, Xiaobo Li56,396, Xiaotong Li5, Xinyue Li56, Yilong Li49, Han Liang21, Sheng-Ben Liang234, Peter Lichter68,397, Pei Lin8, Ziao Lin8,487, W. M. Linehan488, Ole Christian Lingjærde489, Dongbing Liu56,396, Eric Minwei Liu88,301,302, Fei-Fei Liu201,490, Fenglin Liu381,491, Jia Liu492, Xingmin Liu56,396, Julie Livingstone6, Dimitri Livitz8, Naomi Livni221, Lucas Lochovsky5,19,110, Markus Loeffler467, Georgina V. Long211, Armando Lopez-Guillermo493, Shaoke Lou5,19, David N. Louis72, Laurence B. Lovat117, Yiling Lu38, Yong-Jie Lu162,494, Youyong Lu495,496,497, Claudio Luchini167, Ilinca Lungu144,148, Xuemei Luo152, Hayley J. Luxton117, Andy G. Lynch223,315,498, Lisa Lype36, Cristina López111,112, Carlos López-Otín499, Eric Z. Ma64, Yussanne Ma30, Gaetan MacGrogan500, Shona MacRae501, Geoff Macintyre223, Tobias Madsen73, Kazuhiro Maejima184, Andrea Mafficini177, Dennis T. Maglinte166,502, Arindam Maitra174, Partha P. Majumder174, Luca Malcovati232, Salem Malikic62,78, Giuseppe Malleo115, Graham J. Mann211,246,503, Luisa Mantovani-Löffler504, Kathleen Marchal505,506, Giovanni Marchegiani115, Elaine R. Mardis1,193,507, Adam A. Margolin31, Maximillian G. Marin37, Florian Markowetz223,315, Julia Markowski403, Jeffrey Marks508, Tomas Marques-Bonet61,81,386,509, Marco A. Marra30, Luke Marsden351, John W. M. Martens332, Sancha Martin49,510, Jose I. Martin-Subero81,511, Iñigo Martincorena49, Alexander Martinez-Fundichely301,302,451 Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Charlie E. Massie223, Thomas J. Matthew37, Lucy Matthews178, Erik Mayer221,512, Simon Mayes513, Michael Mayo30, Faridah Mbabaali272, Karen McCune514, Ultan McDermott49, Patrick D. McGillivray19, Michael D. McLellan1,3,20, John D. McPherson148,272,515, John R. McPherson179,180, Treasa A. McPherson260, Samuel R. Meier8, Alice Meng516, Shaowu Meng134, Andrew Menzies49, Neil D. Merrett115,517, Sue Merson178, Matthew Meyerson8,14,82, William U. Meyerson4,5, Piotr A. Mieczkowski518, George L. Mihaiescu76, Sanja Mijalkovic284, Ana Mijalkovic Mijalkovic-Lazic284, Tom Mikkelsen519, Michele Milella253, Linda Mileshkin103, Christopher A. Miller1 , David K. Miller113,170, Jessica K. Miller272, Gordon B. Mills33,34, Ana Milovanovic57, Sarah Minner520, Marco Miotto115, Gisela Mir Arnau267, Lisa Mirabello240, Chris Mitchell103, Thomas J. Mitchell49,315,352, Satoru Miyano375, Naoki Miyoshi375, Shinichi Mizuno521, Fruzsina Molnár-Gábor522, Malcolm J. Moore300, Richard A. Moore30, Sandro Morganella49, Quaid D. Morris127,490, Carl Morrison523,524, Lisle E. Mose134, Catherine D. Moser349, Ferran Muiños298,299, Loris Mularoni298,299, Andrew J. Mungall30, Karen Mungall30, Elizabeth A. Musgrove133, Ville Mustonen525,526,527, David Mutch528, Francesc Muyas52,53,74, Donna M. Muzny12, Alfonso Muñoz59, Jerome Myers529, Ola Myklebost461, Peter Möller530, Genta Nagae89, Adnan M. Nagrial170, Hardeep K. Nahal-Bose76, Hitoshi Nakagama531, Hidewaki Nakagawa184, Hiromi Nakamura118, Toru Nakamura388, Kaoru Nakano184, Tannistha Nandi532, Jyoti Nangalia49, Mia Nastic284, Arcadi Navarro61,81,386, Fabio C. P. Navarro19, David E. Neal223,352, Gerd Nettekoven533, Felicity Newell209,311, Steven J. Newhouse59, Yulia Newton37, Alvin Wei Tian Ng534, Anthony Ng535, Jonathan Nicholson49, David Nicol221, Yongzhan Nie321,536, G. Petur Nielsen72, Morten Muhlig Nielsen73, Serena Nik-Zainal49,281,282,537, Michael S. Noble8, Katia Nones209,311, Paul A. Northcott538, Faiyaz Notta148,539, Brian D. O’Connor76,540, Peter O’Donnell541, Maria O’Donovan282, Sarah O’Meara49, Brian Patrick O’Neill542, J. Robert O’Neill543, David Ocana59, Angelica Ochoa88, Layla Oesper544, Christopher Ogden221, Hideki Ohdan93, Kazuhiro Ohi375, Lucila Ohno-Machado244, Karin A. Oien523,545, Akinyemi I. Ojesina546,547,548, Hidenori Ojima549, Takuji Okusaka550, Larsson Omberg551, Choon Kiat Ong552, Stephan Ossowski52,53,74, German Ott553, B. F. Francis Ouellette76,554, Christine P’ng6, Marta Paczkowska6, Salvatore Paiella115, Chawalit Pairojkul523, Marina Pajic170, Qiang Pan-Hammarström56,555, Elli Papaemmanuil49, Irene Papatheodorou59, Nagarajan Paramasivam43,47, Ji Wan Park385, Joong-Won Park556, Keunchil Park557,558, Kiejung Park559, Peter J. Park264,266, Joel S. Parker518, Simon L. Parsons124, Harvey Pass560, Danielle Pasternack272, Alessandro Pastore276, Ann-Marie Patch209,311, Iris Pauporté251, Antonio Pea115, John V. Pearson209,311, Chandra Sekhar Pedamallu8,14,82, Jakob Skou Pedersen73,75, Paolo Pederzoli115, Martin Peifer270, Nathan A. Pennell561, Charles M. Perou129,518, Marc D. Perry76,77, Gloria M. Petersen562, Myron Peto22, Nicholas Petrelli563, Robert Petryszak59, Stefan M. Pfister426,465,564, Mark Phillips424, Oriol Pich298,299, Hilda A. Pickett478, Todd D. Pihl565, Nischalan Pillay566, Sarah Pinder567, Mark Pinese170, Andreia V. Pinho568, Esa Pitkänen60, Xavier Pivot569, Elena Piñeiro-Yáñez95, Laura Planko533, Christoph Plass345, Paz Polak8,14,15, Tirso Pons570, Irinel Popescu571, Olga Potapova572, Aparna Prasad52, Shaun R. Preston573, Manuel Prinz47, Antonia L. Pritchard311, Stephenie D. Prokopec6, Elena Provenzano574, Xose S. Puente499, Sonia Puig176, Montserrat Puiggròs57, Sergio Pulido-Tamayo505,506, Gulietta M. Pupo246, Colin A. Purdie575, Michael C. Quinn209,311, Raquel Rabionet52,53,576, Janet S. Rader577, Bernhard Radlwimmer397, Petar Radovic284, Benjamin Raeder60, Keiran M. Raine49, Manasa Ramakrishna49, Kamna Ramakrishnan49, Suresh Ramalingam578, Benjamin J. Raphael579, W. Kimryn Rathmell580, Tobias Rausch60, Guido Reifenberger475, Jüri Reimand6,44, Jorge Reis-Filho348, Victor Reuter348, Iker Reyes-Salazar298, Matthew A. Reyna579, Sheila M. Reynolds36, Esther Rheinbay8,14,72, Yasser Riazalhosseini189, Andrea L. Richardson323, Julia Richter111,128, Matthew Ringel581, Markus Ringnér181, Yasushi Rino582, Karsten Rippe405, Jeffrey Roach583, Lewis R. Roberts349, Nicola D. Roberts49, Steven A. Roberts584, A. Gordon Robertson30, Alan J. Robertson113, Javier Bartolomé Rodriguez57, Bernardo Rodriguez-Martin104,105,106, F. Germán Rodríguez-González83,332, Michael H. A. Roehrl44,123,148,234,585,586, Marius Rohde587, Hirofumi Rokutan440, Gilles Romieu588, Ilse Rooman170, Tom Roques262, Daniel Rosebrock8, Mara Rosenberg8,72, Philip C. Rosenstiel589, Andreas Rosenwald590, Edward W. Rowe221,591, Romina Royo57, Steven G. Rozen179,180,592, Yulia Rubanova17,127, Mark A. Rubin423,593,594,595,596, Carlota Rubio-Perez298,299,597, Vasilisa A. Rudneva60, Borislav C. Rusev177, Andrea Ruzzenente598, Gunnar Rätsch276,277,278,279,280,430, Radhakrishnan Sabarinathan298,299,599, Veronica Y. Sabelnykova6, Sara Sadeghi30, S. Cenk Sahinalp62,78,79, Natalie Saini357, Mihoko Saito-Adachi440, Gordon Saksena8, Adriana Salcedo6, Roberto Salgado600, Leonidas Salichos5,19, Richard Sallari8, Charles Saller601, Roberto Salvia115, Michelle Sam272, Jaswinder S. Samra115,602, Francisco Sanchez-Vega114,121, Chris Sander276,603,604, Grant Sanders134, Rajiv Sarin605, Iman Sarrafi62,78, Aya Sasaki-Oku184, Torill Sauer489, Guido Sauter520, Robyn P. M. Saw211, Maria Scardoni167, Christopher J. Scarlett170,606, Aldo Scarpa177, Ghislaine Scelo194, Dirk Schadendorf68,607, Jacqueline E. Schein30, Markus B. Schilhabel589, Matthias Schlesner47,80, Thorsten Schlomm84,608, Heather K. Schmidt1 , Sarah-Jane Schramm246, Stefan Schreiber609, Nikolaus Schultz121, Steven E. Schumacher8,323, Roland F. Schwarz59,403,405,610, Richard A. Scolyer211,448,602, David Scott428, Ralph Scully611, Raja Seethala612, Ayellet V. Segre8,613, Iris Selander260, Colin A. Semple434, Yasin Senbabaoglu276, Subhajit Sengupta614, Elisabetta Sereni115, Stefano Serra585, Dennis C. Sgroi72, Mark Shackleton103, Nimish C. Shah352, Sagedeh Shahabi234, Catherine A. Shang329, Ping Shang211, Ofer Shapira8,323, Troy Shelton271, Ciyue Shen603,604, Hui Shen615, Rebecca Shepherd49, Ruian Shi490, Yan Shi134, Yu-Jia Shiah6, Tatsuhiro Shibata118,616, Juliann Shih8,82, Eigo Shimizu375, Kiyo Shimizu617, Seung Jun Shin618, Yuichi Shiraishi375, Tal Shmaya285, Ilya Shmulevich36, Solomon I. Shorser6, Charles Short59, Raunak Shrestha62, Suyash S. Shringarpure217, Craig Shriver619, Shimin Shuai6,126, Nikos Sidiropoulos83, Reiner Siebert112,620, Anieta M. Sieuwerts332, Lina Sieverling205,237, Sabina Signoretti202,621, Katarzyna O. Sikora177, Michele Simbolo138, Ronald Simon520, Janae V. Simons134, Jared T. Simpson6,17, Peter T. Simpson473, Samuel Singer115,458, Nasa Sinnott-Armstrong8,217, Payal Sipahimalani30, Tara J. Skelly390, Marcel Smid332, Jaclyn Smith622, Karen Smith-McCune514, Nicholas D. Socci276, Heidi J. Sofia27, Matthew G. Soloway134, Lei Song240, Anil K. Sood623,624,625, Sharmila Sothi626, Christos Sotiriou244, Cameron M. Soulette37, Paul N. Span627, Paul T. Spellman22, Nicola Sperandio177, Andrew J. Spillane211, Oliver Spiro8, Jonathan Spring628, Johan Staaf181, Peter F. Stadler163,164,165, Peter Staib629, Stefan G. Stark277,279,618,630, Lucy Stebbings49, Ólafur Andri Stefánsson631, Oliver Stegle59,60,632, Lincoln D. Stein6,126, Alasdair Stenhouse633, Chip Stewart8, Stephan Stilgenbauer634, Miranda D. Stobbe52,61, Michael R. Stratton49, Jonathan R. Stretch211, Adam J. Struck31, Joshua M. Stuart24,37, Henk G. Stunnenberg396,635, Hong Su56,396, Xiaoping Su99, Ren X. Sun6, Stephanie Sungalee60, Hana Susak52,53, Akihiro Suzuki89,636, Fred Sweep637, Monika Szczepanowski128, Holger Sültmann67,638, Takashi Yugawa617, Angela Tam30, David Tamborero298,299, Benita Kiat Tee Tan639, Donghui Tan518, Patrick Tan180,532,592,640, Hiroko Tanaka375, Hirokazu Taniguchi616, Tomas J. Tanskanen641, Maxime Tarabichi49,290, Roy Tarnuzzer220, Patrick Tarpey642, Morgan L. Taschuk152, Kenji Tatsuno89, Simon Tavaré223,643, Darrin F. Taylor113, Amaro Taylor-Weiner8, Jon W. Teague49, Bin Tean Teh180,592,640,644,645, Varsha Tembe246, Javier Temes104,105, Kevin Thai76, Sarah P. Thayer393, Nina Thiessen30, Gilles Thomas646, Sarah Thomas221, Alan Thompson221, Alastair M. Thompson633, John F. Thompson211, R. Houston Thompson647, Heather Thorne103, Leigh B. Thorne176, Adrian Thorogood424, Grace Tiao8, Nebojsa Tijanic284, Lee E. Timms272, Roberto Tirabosco648, Marta Tojo106, Stefania Tommasi649, Christopher W. Toon170, Umut H. Toprak48,650, David Torrents57,81, Giampaolo Tortora651,652, Jörg Tost653, Yasushi Totoki118, David Townend654, Nadia Traficante103, Isabelle Treilleux655,656, Jean-Rémi Trotta61, Lorenz H. P. Trümper469, Ming Tsao124,539, Tatsuhiko Tsunoda183,657,658,659, Jose M. C. Tubio104,105,106, Olga Tucker660, Richard Turkington661, Daniel J. Turner513, Andrew Tutt323, Masaki Ueno376, Naoto T. Ueno662, Christopher Umbricht151,213,663, Husen M. Umer305,664, Timothy J. Underwood665, Lara Urban59,60, Tomoko Urushidate616, Tetsuo Ushiku339, Liis Uusküla-Reimand666,667, Alfonso Valencia57,81, David J. Van Den Berg166, Steven Van Laere307, Peter Van Loo290,291, Erwin G. Van Meir668, Gert G. Van den Eynden307, Theodorus Van der Kwast123, Naveen Vasudev137, Miguel Vazquez57,669, Ravikiran Vedururu267, Umadevi Veluvolu518, Shankar Vembu490,670, Lieven P. C. Verbeke506,671, Peter Vermeulen307, Clare Verrill351,672, Alain Viari177, David Vicente57, Caterina Vicentini177, K. Vijay Raghavan365, Juris Viksna673, Ricardo E. Vilain674, Izar Villasante57, Anne Vincent-Salomon635, Tapio Visakorpi190, Douglas Voet8, Paresh Vyas311,351, Ignacio Vázquez-García49,86,675,676, Nick M. Waddell209, Nicola Waddell209,311, Claes Wadelius677, Lina Wadi6, Rabea Wagener111,112, Jeremiah A. Wala8,14,82, Jian Wang56, Jiayin Wang1,40,66, Linghua Wang12, Qi Wang465, Wenyi Wang21, Yumeng Wang21, Zhining Wang220, Paul M. Waring523, Hans-Jörg Warnatz483, Jonathan Warrell5,19, Anne Y. Warren352,678, Sebastian M. Waszak60, David C. Wedge49,294,679, Dieter Weichenhan345, Paul Weinberger680, John N. Weinstein38, Joachim Weischenfeldt60,83,84, Daniel J. Weisenberger166, Ian Welch681, Michael C. Wendl1,10,11, Johannes Werner47,85, Justin P. Whalley61,682, David A. Wheeler12,13, Hayley C. Whitaker117, Dennis Wigle683, Matthew D. Wilkerson518, Ashley Williams244, James S. Wilmott211, Gavin W. Wilson6,148, Julie M. Wilson148, Richard K. Wilson1,684, Boris Winterhoff685, Jeffrey A. Wintersinger17,127,384, Maciej Wiznerowicz686,687, Stephan Wolf688, Bernice H. Wong689, Tina Wong1,30, Winghing Wong690, Youngchoon Woo250, Scott Wood209,311, Bradly G. Wouters44, Adam J. Wright6, Derek W. Wright133,691, Mark H. Wright217, Chin-Lee Wu72, Dai-Ying Wu285, Guanming Wu692, Jianmin Wu170, Kui Wu56,396, Yang Wu179,180, Zhenggang Wu64, Liu Xi12, Tian Xia693, Qian Xiang76, Xiao Xiao66, Rui Xing497, Heng Xiong56,396, Qinying Xu209,311, Yanxun Xu694, Hong Xue64, Shinichi Yachida118,695, Sergei Yakneen60, Rui Yamaguchi375, Takafumi N. Yamaguchi6, Masakazu Yamamoto120, Shogo Yamamoto89, Hiroki Yamaue376, Fan Yang490, Huanming Yang56, Jean Y. Yang696, Liming Yang220, Lixing Yang697, Shanlin Yang306, Tsun-Po Yang270, Yang Yang369, Xiaotong Yao408,698, Marie-Laure Yaspo483, Lucy Yates49, Christina Yau156, Chen Ye56,396, Kai Ye40,41, Venkata D. Yellapantula20,86, Christopher J. Yoon249, Sung-Soo Yoon463, Fouad Yousif6, Jun Yu699, Kaixian Yu700, Willie Yu701, Yingyan Yu702, Ke Yuan223,510,703, Yuan Yuan21, Denis Yuen6, Takashi Yugawa617, Christina K. Yung76, Olga Zaikova704, Jorge Zamora49,104,105,106, Marc Zapatka397, Jean C. Zenklusen220, Thorsten Zenz67, Nikolajs Zeps705,706, Cheng-Zhong Zhang8,707, Fan Zhang381, Hailei Zhang8, Hongwei Zhang494, Hongxin Zhang121, Jiashan Zhang220, Jing Zhang5, Junjun Zhang76, Xiuqing Zhang56, Xuanping Zhang66,369, Yan Zhang5,708,709, Zemin Zhang381,710, Zhongming Zhao711, Liangtao Zheng381, Xiuqing Zheng381, Wanding Zhou615, Yong Zhou56, Bin Zhu240, Hongtu Zhu700,712, Jingchun Zhu24, Shida Zhu56,396, Lihua Zou713, Xueqing Zou49, Anna deFazio246,247,714, Nicholas van As221, Carolien H. M. van Deurzen715, Marc J. van de Vijver523, L. van’t Veer716 & Christian von Mering433,717, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

Growth and fatness traits are complex and economically important traits in the pig industry. The molecular basis underlying porcine growth and fatness traits remains largely unknown. To uncover genetic loci and candidate genes for these traits, we explored the GeneSeek GGP Porcine 80K SNP chip to perform a GWAS for seven growth and fatness traits in 365 individuals from the Sujiang pig, a recently developed breed in China. We identified two, 17, one and 11 SNPs surpassing the suggestively significant threshold (P < 1.86 × 10-5 ) for body weight, chest circumference, chest width and backfat thickness respectively. Of these SNPs, 20 represent novel genetic loci, and five and four SNPs were respectively associated with chest circumference and backfat thickness at a genome-wide significant threshold (P < 9.31 × 10-7 ). Eight SNPs had a pleiotropic effect on both chest circumference and backfat thickness. The most remarkable locus resided in a region between 72.95 and 76.27 Mb on pig chromosome 4, harboring a number of previously reported quantitative trait loci related to backfat deposition. In addition to two reported genes (PLAG1 and TAS2R38), we identified four genes including GABRB3, ZNF106, XKR4 and MGAM as novel candidates for body weight and backfat thickness at the mapped loci. Our findings provide insights into the genetic architecture of porcine growth and fatness traits and potential markers for selective breeding of Chinese Sujiang pigs.

We selected epitope-specific antibodies by cross-linking hits to the site-specific photocrosslinker on the target antigen., Currently, there are no methods available offering solutions to select and identify antibodies binding to a specific conformational epitope of an antigen. Here, we developed a method to allow epitope-directed antibody selection from a phage display library by photocrosslinking bound antibodies to a site that specifically incorporates a noncanonical amino acid, p-benzoyl-l-phenylalanine (pBpa), on the target antigen epitope. By one or two rounds of panning against antibody phage display libraries, those hits that covalently bind to the proximity site of pBpa on specific epitopes of target antigens after ultraviolet irradiation are enriched and selected. This method was applied to specific epitopes on human interleukin-1β and complement 5a. In both cases, more than one-third of hits identified bind to the target epitopes, demonstrating the feasibility and versatility of this method.

Biofilms are microbial communities embedded in extracellular matrix. Exopolysaccharide Psl (ePsl) is a key biofilm matrix component that initiates attachment, maintains biofilms architecture, and protects bacteria within biofilms of Pseudomonas aeruginosa, an opportunistic pathogen. There are at least 12 Psl proteins involved in the biosynthesis of this exopolysaccharide. However, it remains unclear about the function of each Psl protein and how these proteins work together during the biosynthesis of ePsl. PslG has been characterized as a degrader of ePsl in extracellular or periplasm and PslD is predicted to be a transporter. In this study, we found that PslG and its glycoside hydrolytic activity were also involved in the biosynthesis of ePsl. PslG localized mainly in the inner membrane and some in the periplasm. The inner membrane association of PslG was critical for the biosynthesis of ePsl. The expression of PslA, PslD, and PslE helped PslG remain in the inner membrane. The bacterial two‐hybrid results suggested that PslE could interacted with either PslA, PslD, or PslG. The strongest interaction was found between PslE and PslD. Consistently, PslD was disabled to localize on the outer membrane in the ΔpslE strain, suggesting that the PslE‐PslD interaction affected the localization of PslD. Our results shed light on the assembly of ePsl biosynthesis machinery and suggested that the membrane‐associated PslG was a part of ePsl biosynthesis proteins complex., In this study, we shed light on the assembly of exopolysaccharide Psl (ePsl) biosynthesis machinery in Pseudomonas aeruginosa. We improve our understanding about the function of PslG, the interactions among Psl proteins and the effect of proteins interactions on the localization of Psl protein during ePsl biosynthesis .

The gut microbiome provides resistance to infection. However, the mechanisms for this are poorly understood. Colonization with the intestinal bacteriumClostridium scindensprovided protection from the parasiteEntamoeba histolyticavia innate immunity. Introduction ofC. scindensinto the gut microbiota epigenetically altered and expanded bone marrow granulocyte-monocyte-progenitors (GMPs) and provided neutrophil-mediated protection against subsequent challenge withE. histolytica. Adoptive transfer of bone-marrow fromC. scindenscolonized-mice into naïve-mice protected against ameba infection and increased intestinal neutrophils. Because of the known ability ofC. scindensto metabolize the bile salt cholate, we measured deoxycholate and discovered that it was increased in the sera ofC. scindenscolonized mice, as well as in children protected from amebiasis. Administration of deoxycholate alone (in the absence ofC. scindens) increased the epigenetic mediator JMJD3 and GMPs and provided protection from amebiasis. In conclusion the microbiota was shown to communicate to the bone marrow via microbially-metabolized bile salts to train innate immune memory to provide antigen-nonspecific protection from subsequent infection. This represents a novel mechanism by which the microbiome protects from disease.One Sentence SummaryIntroduction of the human commensal bacteriaClostridium scindensinto the intestinal microbiota epigenetically alters bone marrow and protects from future parasite infection.

Background The mammalian major histocompatibility complex (MHC) harbours clusters of genes associated with the immunological defence of animals against infectious pathogens. At present, no complete MHC physical map is available for any of the wild ruminant species in the world. Results The high-density physical map is composed of two contigs of 47 overlapping bacterial artificial chromosome (BAC) clones, with an average of 115 Kb for each BAC, covering the entire addax MHC genome. The first contig has 40 overlapping BAC clones covering an approximately 2.9 Mb region of MHC class I, class III, and class IIa, and the second contig has 7 BAC clones covering an approximately 500 Kb genomic region that harbours MHC class IIb. The relative position of each BAC corresponding to the MHC sequence was determined by comparative mapping using PCR screening of the BAC library of 192,000 clones, and the order of BACs was determined by DNA fingerprinting. The overlaps of neighboring BACs were cross-verified by both BAC-end sequencing and co-amplification of identical PCR fragments within the overlapped region, with their identities further confirmed by DNA sequencing. Conclusions We report here the successful construction of a high-quality physical map for the addax MHC region using BACs and comparative mapping. The addax MHC physical map we constructed showed one gap of approximately 18 Mb formed by an ancient autosomal inversion that divided the MHC class II into IIa and IIb. The autosomal inversion provides compelling evidence that the MHC organizations in all of the ruminant species are relatively conserved. Electronic supplementary material The online version of this article (10.1186/s12864-019-5790-2) contains supplementary material, which is available to authorized users.

Ethnopharmacological relevance Chinese medicinal herbs have long been recognized as important resources that can be used for the struggle against diseases and a significant component of health care system for thousands of years. Aim of the study In order to understand their roles in the treatment against bacterial infections, we examined the underlying mechanisms of one of the medicinal herb extracts (MHE) (Artemisiae argyi Folium, the root bark of Cortex dictamni and the root of Solanum melongena) on the human opportunistic pathogen Pseudomonas aeruginosa. Materials and methods We combined phenotypic assays, transcriptional analysis and chemical investigations to identify the mechanisms underlying MHE inhibition. The standard sample was prepared and transcriptional reporters for quorum sensing systems were constructed. Electrophoretic mobility shift assays were used to clarify the mechanism. GC-MS and molecular docking were used to identify the chemicals in MHE and potential binding agents. Results We found that co-culturing of MHE with bacterial cells did not change the growth rate but substantially attenuate the production of virulence factors such as phenazine pyocyanin, siderophore pyoverdine and biofilm formation. Transcriptional responses of three major quorum sensing (QS) systems of P. aeruginosa to MHE showed that Pseudomonas quinolone signaling (PQS) system was completely repressed, rhlR/rhlI QS system was moderately inhibited, while lasR/lasI QS system was only slightly affected, suggesting that MHE might selectively target the PQS system to inhibit bacterial virulence. Furthermore, electrophoretic mobility shift assays (EMSA) showed that MHE inhibited the binding of MvfR the corresponding pqsA promoter region, suggesting that MHE serves as a competitive agent to quench the QS functionality in P. aeruginosa. Conclusion We prove that MHE functions as an effective countermeasure against bacterial infections.

Airway epithelial cells and macrophages differ markedly in their responses to influenza A virus (IAV) infection. To investigate transcriptional responses underlying these differences, purified subsets of type II airway epithelial cells (ATII) and alveolar macrophages (AM) recovered from the lungs of mock- or IAV-infected mice at 9 h postinfection were subjected to RNA sequencing. This time point was chosen to allow for characterization of cell types first infected with the virus inoculum, prior to multicycle virus replication and the infiltration of inflammatory cells into the airways. In the absence of infection, AM predominantly expressed genes related to immunity, whereas ATII expressed genes consistent with their physiological roles in the lung. Following IAV infection, AM almost exclusively activated cell-intrinsic antiviral pathways that were dependent on interferon (IFN) regulatory factor 3/7 (IRF3/7) and/or type I IFN signaling. In contrast, IAV-infected ATII activated a broader range of physiological responses, including cell-intrinsic antiviral pathways, which were both independent of and dependent on IRF3/7 and/or type I IFN. These data suggest that transcriptional profiles hardwired during development are a major determinant underlying the different responses of ATII and AM to IAV infection. IMPORTANCE Airway epithelial cells (AEC) and airway macrophages (AM) represent major targets of influenza A virus (IAV) infection in the lung, yet the two cell types respond very differently to IAV infection. We have used RNA sequencing to define the host transcriptional responses in each cell type under steady-state conditions as well as following IAV infection. To do this, different cell subsets isolated from the lungs of mock- and IAV-infected mice were subjected to RNA sequencing. Under steady-state conditions, AM and AEC express distinct transcriptional activities, consistent with distinct physiological roles in the airways. Not surprisingly, these cells also exhibited major differences in transcriptional responses following IAV infection. These studies shed light on how the different transcriptional architectures of airway cells from two different lineages drive transcriptional responses to IAV infection.

Background & Aims Environmental enteric dysfunction (EED) limits the Sustainable Development Goals of improved childhood growth and survival. We applied mucosal genomics to advance our understanding of EED. Methods The Study of Environmental Enteropathy and Malnutrition (SEEM) followed 416 children from birth to 24 months in a rural district in Pakistan. Biomarkers were measured at 9 months and tested for association with growth at 24 months. The duodenal methylome and transcriptome were determined in 52 undernourished SEEM participants and 42 North American controls and patients with celiac disease. Results After accounting for growth at study entry, circulating insulin-like growth factor-1 (IGF-1) and ferritin predicted linear growth, whereas leptin correlated with future weight gain. The EED transcriptome exhibited suppression of antioxidant, detoxification, and lipid metabolism genes, and induction of anti-microbial response, interferon, and lymphocyte activation genes. Relative to celiac disease, suppression of antioxidant and detoxification genes and induction of antimicrobial response genes were EED-specific. At the epigenetic level, EED showed hyper-methylation of epithelial metabolism and barrier function genes, and hypo-methylation of immune response and cell proliferation genes. Duodenal coexpression modules showed association between lymphocyte proliferation and epithelial metabolic genes and histologic severity, fecal energy loss, and wasting (weight-for-length/height Z < -2.0). Leptin was associated with expression of epithelial carbohydrate metabolism and stem cell renewal genes. Immune response genes were attenuated by giardia colonization. Conclusions Children with reduced circulating IGF-1 are more likely to experience stunting. Leptin and a gene signature for lymphocyte activation and dysregulated lipid metabolism are implicated in wasting, suggesting new approaches for EED refractory to nutritional intervention. ClinicalTrials.gov, Number: NCT03588013. (https://clinicaltrials.gov/ct2/show/NCT03588013), Graphical abstract

Summary: Lymph nodes (LNs) are constructed of intricate networks of endothelial and mesenchymal stromal cells. How these lymphoid stromal cells (LSCs) regulate lymphoid tissue remodeling and contribute to immune responses remains poorly understood. We performed a comprehensive functional and transcriptional analysis of LSC responses to skin viral infection and found that LSC subsets responded robustly, with different kinetics for distinct pathogens. Recruitment of cells to inflamed LNs induced LSC expansion, while B cells sustained stromal responses in an antigen-independent manner. Infection induced rapid transcriptional responses in LSCs. This transcriptional program was transient, returning to homeostasis within 1 month of infection, yet expanded fibroblastic reticular cell networks persisted for more than 3 months after infection, and this altered LN composition reduced the magnitude of LSC responses to subsequent heterologous infection. Our results reveal the complexity of LSC responses during infection and suggest that amplified networks of LN stromal cells support successive immune responses. : Lymph nodes are constructed of intricate networks of stromal cells. Gregory et al. reveal a robust yet transient transcriptional program in lymph node stromal cells induced by virus infection. Previously primed lymph nodes sustained amplified networks of stromal cells that supported subsequent immune responses. Keywords: lymphoid stromal cells, fibroblastic reticular cells, endothelial cells, lymph nodes, immune response, virus infection, lymphocytes

Vascular endothelial growth factor-2 receptor (VEGFR-2) kinase is a promising target for the development of novel anticancer drugs. Molecular docking modeling was performed on a series of 4-aryl-1,4-dihydropyridines derivatives to evaluate the structural basis for VEGFR-2 inhibitory activity. Some 4-aryl-1,4-dihydropyridines were synthesized in the reaction of aromatic aldehydes and ethyl propiolate with anilines in acetic acid. The biological activities were evaluated against the cells A549, A431 and Hep-G2. The results indicated that 4-aryl-1,4-dihydropyridines could be the promising potential VEGFR-2 inhibitors.

In recent years, various intervention strategies have reduced malaria morbidity and mortality, but further improvements probably depend upon development of a broadly protective vaccine. To better understand immune requirement for protection, we examined liver-stage immunity after vaccination with irradiated sporozoites, an effective though logistically difficult vaccine. We identified a population of memory CD8+ T cells that expressed the gene signature of tissue-resident memory T (Trm) cells and remained permanently within the liver, where they patrolled the sinusoids. Exploring the requirements for liver Trm cell induction, we showed that by combining dendritic cell-targeted priming with liver inflammation and antigen recognition on hepatocytes, high frequencies of Trm cells could be induced and these cells were essential for protection against malaria sporozoite challenge. Our study highlights the immune potential of liver Trm cells and provides approaches for their selective transfer, expansion, or depletion, which may be harnessed to control liver infections or autoimmunity.

Summary Cyclic diguanosine monophosphate (c-di-GMP) is one of the most important bacterial second messengers that controls many bacterial cellular functions including lifestyle switch between plankton and biofilm. Surface attachment defective (SadC) is a diguanylate cyclase (DGC) involved in the biosynthesis of c-di-GMP in Pseudomonas aeruginosa, an opportunistic pathogen that can cause diverse infections. Here we report the crystal structure of GGDEF domain from SadC and the critical role of the trans-membrane (TM) domain of SadC with regard to biofilm formation, exopolysaccharide production and motility. We showed that over-expression of SadC in P. aeruginosa PAO1 totally inhibited swimming motility and significantly enhanced the production of exopolysaccharide Psl. SadC lacking TM domains (SadC300–487) could not localize on cytoplasmic membrane and form cluster, lost the ability to inhibit the swimming and twitching motility, and showed the attenuated activity to promote Psl production despite that SadC300–487 was able to catalyze the synthesize of c-di-GMP in vitro and in vivo. The GGDEF domain of SadC has a typical GGDEF structure and the α-helix connected the TM domains with SadC GGDEF domain is essential for SadC to form DGC oligomers. Our data imply that membrane association of SadC promotes its DGC activity by affecting the formation of active DGC oligomers.

Reticulocalbin 3 (Rcn3) is an endoplasmic reticulum lumen protein localized to the secretory pathway. As a Ca2t-binding protein of 45 kDa (Cab45)/Rcn/ER Ca2t-binding protein of 55 kDa (ERC45)/calumenin (CREC) family member, Rcn3 is reported to function as a chaperone protein involved in protein synthesis and secretion; however, the biological role of Rcn3 is largely unknown. The results presented here, for the first time, depict an indispensable physiological role of Rcn3 in perinatal lung maturation by using an Rcn3 gene knockout mouse model. These mutant mice die immediately at birth owing to atelectasis-induced neonatal respiratory distress, although these embryos are produced with grossly normal development. This respiratory distress results from a failure of functional maturation of alveolar epithelial type II cells during alveogenesis. This immaturity of type II cells is associated with a dramatic reduction in surfactant protein A and D, a disruption in surfactant phospholipid homeostasis, and a disorder in lamellar body. In vitro studies further show that Rcn3 deficiency blunts the secretion of surfactant proteins and phospholipids from lung epithelial cells, suggesting a decrease in availability of surfactants for their surface activity. Collectively, these observations indicate an essential role of Rcn3 in perinatal lung maturation and neonatal respiratory adaptation as well as shed additional light on the mechanism of neonatal respiratory distress syndrome development.