Summary
SecReT6 (http://db‐mml.sjtu.edu.cn/SecReT6/) is an integrated database providing comprehensive information on type VI secretion systems (T6SSs) in bacteria. T6SSs are a class of sophisticated ...cell contact‐dependent apparatuses involved in mediating antagonistic or synergistic communications between bacteria and/or bacteria and eukaryotes. These apparatuses have recently been found to be widely distributed among Gram‐negative bacterial species. SecReT6 offers a unique, readily explorable archive of known and putative T6SSs, and cognate effectors found in bacteria. It currently contains data on 11 167 core T6SS components mapping to 906 T6SSs found in 498 bacterial strains representing 240 species, as well as a collection of over 600 directly relevant references. Also collated and archived were 1340 diverse candidate secreted effectors which were experimentally shown and/or predicted to be delivered by T6SSs into target eukaryotic and/or prokaryotic cells as well as 196 immunity proteins. A broad range of T6SS gene cluster detection and comparative analysis tools are readily accessible via SecReT6, which may aid identification of effectors and immunity proteins around the T6SS core components. This database will be regularly updated to ensure its ongoing maximal utility and relevance to the scientific research community.
Porcine mucin has been commonly used to enhance the infectivity of bacterial pathogens, including
, in animal models, but the mechanisms for enhancement by mucin remain relatively unknown. In this ...study, using the mouse model of intraperitoneal (i.p.) mucin-enhanced
infection, we characterized the kinetics of bacterial replication and dissemination and the host innate immune responses, as well as their potential contribution to mucin-enhanced bacterial virulence. We found that mucin, either admixed with or separately injected with the challenge bacterial inoculum, was able to enhance the tissue and blood burdens of
strains of different virulence. Intraperitoneal injection of
-mucin or mucin alone induced a significant but comparable reduction of peritoneal macrophages and lymphocytes, accompanied by a significant neutrophil recruitment and early interleukin-10 (IL-10) responses, suggesting that the resulting inflammatory cellular and cytokine responses were largely induced by the mucin. Depletion of peritoneal macrophages or neutralization of endogenous IL-10 activities showed no effect on the mucin-enhanced infectivity. However, pretreatment of mucin with iron chelator DIBI, but not deferoxamine, partially abolished its virulence enhancement ability, and replacement of mucin with iron significantly enhanced the bacterial burdens in the peritoneal cavity and lung. Taken together, our results favor the hypothesis that iron at least partially contributes to the mucin-enhanced infectivity of
in this model.
Internet of Things (IoT) has emerged as one of the key features of the next-generation wireless networks, where timely delivery of status update packets is essential for many real-time IoT ...applications. To provide users with context-aware services and lighten the transmission burden, the raw data usually need to be preprocessed before being transmitted to the destination. However, the effect of computing on the overall information freshness is not well understood. In this article, we first develop an analytical framework to investigate the information freshness, in terms of peak age of information (PAoI), of a computing-enabled IoT system with multiple sensors. Specifically, we model the procedure of computing and transmission as a tandem queue and derive the analytical expressions of the average PAoI for different sensors. Based on the theoretical results, we formulate a min-max optimization problem to minimize the maximum average PAoI of different sensors. We further design a derivative-free algorithm to find the optimal updating frequency, with which the complexity for checking the convexity of the formulated problem or obtaining the derivatives of the object function can be largely reduced. The accuracy of our analysis and the effectiveness of the proposed algorithm are verified with extensive simulation results.
Gram-positive
bacteria produce thousands of bioactive secondary metabolites, including antibiotics. To systematically investigate genes affecting secondary metabolism, we developed a hyperactive ...transposase-based Tn
transposition system and employed it to mutagenize the model species
, leading to the identification of 51,443 transposition insertions. These insertions were distributed randomly along the chromosome except for some preferred regions associated with relatively low GC content in the chromosomal core. The base composition of the insertion site and its flanking sequences compiled from the 51,443 insertions implied a 19-bp expanded target site surrounding the insertion site, with a slight nucleic acid base preference in some positions, suggesting a relative randomness of Tn
transposition targeting in the high-GC
genome. From the mutagenesis library, 724 mutants involving 365 genes had altered levels of production of the tripyrrole antibiotic undecylprodigiosin (RED), including 17 genes in the RED biosynthetic gene cluster. Genetic complementation revealed that most of the insertions (more than two-thirds) were responsible for the changed antibiotic production. Genes associated with branched-chain amino acid biosynthesis, DNA metabolism, and protein modification affected RED production, and genes involved in signaling, stress, and transcriptional regulation were overrepresented. Some insertions caused dramatic changes in RED production, identifying future targets for strain improvement.
High-GC Gram-positive streptomycetes and related actinomycetes have provided more than 100 clinical drugs used as antibiotics, immunosuppressants, and antitumor drugs. Their genomes harbor biosynthetic genes for many more unknown compounds with potential as future drugs. Here we developed a useful genome-wide mutagenesis tool based on the transposon Tn
for the study of secondary metabolism and its regulation. Using
as a model strain, we found that chromosomal insertion was relatively random, except at some hot spots, though there was evidence of a slightly preferred 19-bp target site. We then used prodiginine production as a model to systematically survey genes affecting antibiotic biosynthesis, providing a global view of antibiotic regulation. The analysis revealed 348 genes that modulate antibiotic production, among which more than half act to reduce production. These might be valuable targets in future investigations of regulatory mechanisms, for strain improvement, and for the activation of silent biosynthetic gene clusters.
In the Internet of Things (IoT) networks, caching is a promising technique to alleviate energy consumption of sensors by responding to users' data requests with the data packets cached in the edge ...caching node (ECN). However, without an efficient status update strategy, the information obtained by users may be stale, which in return would inevitably deteriorate the accuracy and reliability of derived decisions for real-time applications. In this paper, we focus on striking the balance between the information freshness, in terms of age of information (AoI), experienced by users and energy consumed by sensors, by appropriately activating sensors to update their current status. Particularly, we first depict the evolutions of the AoI with each sensor from different users' perspective with time steps of non-uniform duration, which are determined by both the users' data requests and the ECN's status update decision. Then, we formulate a non-uniform time step based dynamic status update optimization problem to minimize the long-term average cost, jointly considering the average AoI and energy consumption. To this end, a Markov Decision Process is formulated and further, a dueling deep R-network based dynamic status update algorithm is devised by combining dueling deep Q-network and tabular R-learning, with which challenges from the curse of dimensionality and unknown of the environmental dynamics can be addressed. Finally, extensive simulations are conducted to validate the effectiveness of our proposed algorithm by comparing it with five baseline deep reinforcement learning algorithms and policies.
Acinetobacter baumannii is an important human pathogen due to its multi-drug resistance. In this study, the genome of an ST10 outbreak A. baumannii isolate LAC-4 was completely sequenced to better ...understand its epidemiology, antibiotic resistance genetic determinants and potential virulence factors. Compared with 20 other complete genomes of A. baumannii, LAC-4 genome harbors at least 12 copies of five distinct insertion sequences. It contains 12 and 14 copies of two novel IS elements, ISAba25 and ISAba26, respectively. Additionally, three novel composite transposons were identified: Tn6250, Tn6251 and Tn6252, two of which contain resistance genes. The antibiotic resistance genetic determinants on the LAC-4 genome correlate well with observed antimicrobial susceptibility patterns. Moreover, twelve genomic islands (GI) were identified in LAC-4 genome. Among them, the 33.4-kb GI12 contains a large number of genes which constitute the K (capsule) locus. LAC-4 harbors several unique putative virulence factor loci. Furthermore, LAC-4 and all 19 other outbreak isolates were found to harbor a heme oxygenase gene (hemO)-containing gene cluster. The sequencing of the first complete genome of an ST10 A. baumannii clinical strain should accelerate our understanding of the epidemiology, mechanisms of resistance and virulence of A. baumannii.
The notion of age-of-information (AoI) is investigated in the context of large-scale wireless networks, in which transmitters need to send a sequence of information packets, which are generated as ...independent Bernoulli processes, to their intended receivers over a shared spectrum. Due to interference, the rate of packet depletion at any given node is entangled with both the spatial configurations, which determine the path loss, and temporal dynamics, which influence the active states, of the other transmitters, resulting in the queues to interact with each other in both space and time over the entire network. To that end, variants in the packet update frequency affect not just the inter-arrival time but also the departure process, and the impact of such phenomena on the AoI is not well understood. In this paper, we establish a theoretical framework to characterize the AoI performance in the aforementioned setting. Particularly, tractable expressions are derived for both the peak and average AoI under two different transmission protocols, namely the first-come-first-serve (FCFS) and the last-come-first-serve with preemption (LCFS-PR). Additionally, our analysis also accounts for the effects of channel access controls such as ALOHA on the AoI. The accuracy of the analysis is verified via simulations, and based on the theoretical outcomes, we find that: <inline-formula> <tex-math notation="LaTeX">i </tex-math></inline-formula>) networks operating under LCFS-PR are able to attain smaller values of peak and average AoI than that under FCFS, whereas the gain is more pronounced when the infrastructure is densely deployed, <inline-formula> <tex-math notation="LaTeX">ii </tex-math></inline-formula>) in sparsely deployed networks, ALOHA with a universally designed channel access probability is not instrumental in reducing the AoI, thus calling for more advanced channel access approaches, and <inline-formula> <tex-math notation="LaTeX">iii </tex-math></inline-formula>) when the infrastructure is densely rolled out, there exists a non-trivial ALOHA channel access probability that minimizes the peak and average AoI under both FCFS and LCFS-PR.
Background
Toll‐like receptor 3 (TLR3) was originally identified as the receptor for viral RNA and represents a major host antiviral defense mechanism. TLR3 may also recognize extracellular RNA ...(exRNA) released from injured tissues under certain stress conditions. However, a role for exRNA and TLR3 in the pathogenesis of myocardial ischemic injury has not been tested. This study examined the role of exRNA and TLR3 signaling in myocardial infarction (MI), apoptosis, inflammation, and cardiac dysfunction during ischemia‐reperfusion (I/R) injury.
Methods and Results
Wild‐type (WT), TLR3−/−, Trif−/−, and interferon (IFN) α/β receptor‐1 deficient (IFNAR1−/−) mice were subjected to 45 minutes of coronary artery occlusion and 24 hours of reperfusion. Compared with WT, TLR3−/− or Trif−/− mice had smaller MI and better preserved cardiac function. Surprisingly, unlike TLR(2/4)‐MyD88 signaling, lack of TLR3‐Trif signaling had no impact on myocardial cytokines or neutrophil recruitment after I/R, but myocardial apoptosis was significantly attenuated in Trif−/− mice. Deletion of the downstream IFNAR1 had no effect on infarct size. Importantly, hypoxia and I/R led to release of RNA including microRNA from injured cardiomyocytes and ischemic heart, respectively. Necrotic cardiomyocytes induced a robust and dose‐dependent cytokine response in cultured cardiomyocytes, which was markedly reduced by RNase but not DNase, and partially blocked in TLR3‐deficient cardiomyocytes. In vivo, RNase administration reduced serum RNA level, attenuated myocardial cytokine production, leukocytes infiltration and apoptosis, and conferred cardiac protection against I/R injury.
Conclusion
TLR3‐Trif signaling represents an injurious pathway during I/R. Extracellular RNA released during I/R may contribute to myocardial inflammation and infarction.
We propose a one-shot, privacy-preserving distributed algorithm to perform logistic regression (ODAL) across multiple clinical sites.
ODAL effectively utilizes the information from the local site ...(where the patient-level data are accessible) and incorporates the first-order (ODAL1) and second-order (ODAL2) gradients of the likelihood function from other sites to construct an estimator without requiring iterative communication across sites or transferring patient-level data. We evaluated ODAL via extensive simulation studies and an application to a dataset from the University of Pennsylvania Health System. The estimation accuracy was evaluated by comparing it with the estimator based on the combined individual participant data or pooled data (ie, gold standard).
Our simulation studies revealed that the relative estimation bias of ODAL1 compared with the pooled estimates was <3%, and the ratio of standard errors was <1.25 for all scenarios. ODAL2 achieved higher accuracy (with relative bias <0.1% and ratio of standard errors <1.05). In real data analysis, we investigated the associations of 100 medications with fetal loss during pregnancy. We found that ODAL1 provided estimates with relative bias <10% for 85% of medications, and ODAL2 has relative bias <10% for 99% of medications. For communication cost, ODAL1 requires transferring p numbers from each site to the local site and ODAL2 requires transferring (p×p+p) numbers from each site to the local site, where p is the number of parameters in the regression model.
This study demonstrates that ODAL is privacy-preserving and communication-efficient with small bias and high statistical efficiency.
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