Spa47 is an oligomerization-activated type three secretion system (T3SS) ATPase from \u3cem\u3eShigella flexneri\u3c/em\u3e

Gram-negative pathogens often use conserved type three secretion systems (T3SS) for virulence. The Shigella type three secretion apparatus (T3SA) penetrates the host cell membrane and provides a unidirectional conduit for injection of effectors into host cells. The protein Spa47 localizes to the base of the apparatus and is speculated to be an ATPase that provides the energy for T3SA formation and secretion. Here, we developed an expression and purification protocol, producing active Spa47 and providing the first direct evidence that Spa47 is a bona fide ATPase. Additionally, size exclusion chromatography and analytical ultracentrifugation identified multiple oligomeric species of Spa47 with the largest greater than 8 fold more active for ATP hydrolysis than the monomer. An ATPase inactive Spa47 point mutant was then engineered by targeting a conserved Lysine within the predicted Walker A motif of Spa47. Interestingly, the mutant maintained a similar oligomerization pattern as active Spa47, but was unable to restore invasion phenotype when used to complement a spa47 null S. flexneri strain. Together, these results identify Spa47 as a Shigella T3SS ATPase and suggest that its activity is linked to oligomerization, perhaps as a regulatory mechanism as seen in some related pathogens. Additionally, Spa47 catalyzed ATP hydrolysis appears to be essential for host cell invasion, providing a strong platform for additional studies dissecting its role in virulence and providing an attractive target for anti-infective agents

Methane observations from the Greenhouse Gases Observing SATellite: Comparison to ground‐based TCCON data and model calculations

We report new short-wave infrared (SWIR) column retrievals of atmospheric methane (X_(CH4)) from the Japanese Greenhouse Gases Observing SATellite (GOSAT) and compare observed spatial and temporal variations with correlative ground-based measurements from the Total Carbon Column Observing Network (TCCON) and with the global 3-D GEOS-Chem chemistry transport model. GOSAT X_(CH4) retrievals are compared with daily TCCON observations at six sites between April 2009 and July 2010 (Bialystok, Park Falls, Lamont, Orleans, Darwin and Wollongong). GOSAT reproduces the site-dependent seasonal cycles as observed by TCCON with correlations typically between 0.5 and 0.7 with an estimated single-sounding precision between 0.4–0.8%. We find a latitudinal-dependent difference between the X_(CH4) retrievals from GOSAT and TCCON which ranges from 17.9 ppb at the most northerly site (Bialystok) to −14.6 ppb at the site with the lowest latitude (Darwin). We estimate that the mean smoothing error difference included in the GOSAT to TCCON comparisons can account for 15.7 to 17.4 ppb for the northerly sites and for 1.1 ppb at the lowest latitude site. The GOSAT X_(CH4) retrievals agree well with the GEOS-Chem model on annual (August 2009 – July 2010) and monthly timescales, capturing over 80% of the zonal variability. Differences between model and observed X_(CH4) are found over key source regions such as Southeast Asia and central Africa which will be further investigated using a formal inverse model analysis

Western corn rootworm (Diabrotica virgifera virgifera LeConte) population dynamics

The western corn rootworm Diabrotica virgifera virgifera LeConte is a major insect pest of field maize, Zea mays L. Larvae can cause substantial injury by feeding on maize roots. Larval feeding may destroy individual roots or root nodes, and reduce plant growth, stability, and yield. Costs associated with managing corn rootworms in continuous maize are annually one of the largest expenditures for insect management in the United States Corn Belt. Even though D. virgifera virgifera has been studied intensively for over 50 years, there is renewed interest in the biology, ecology, and genetics of this species because of its ability to rapidly adapt to management tactics, and its aggressive invasive nature. This article provides a comprehensive review of D. virgifera virgifera population dynamics, specifically: diapause, larval and adult development, seasonality, spatial and temporal dynamics at local and landscape scales, invasiveness in North America and Europe, and non-trophic interactions with other arthropods. Gaps in current knowledge are identified and discussed especially within the context of challenges that scientists in North America and Europe are currently facing regarding pest dynamics and the need to develop appropriate management strategies for each geographic area

Western corn rootworm ( \u3ci\u3eDiabrotica virgifera virgifera\u3c/i\u3e LeConte) population dynamics

1 The western corn rootworm Diabrotica virgifera virgifera LeConte is a major insect pest of field maize, Zea mays L. Larvae can cause substantial injury by feeding on maize roots. Larval feeding may destroy individual roots or root nodes, and reduce plant growth, stability, and yield. Costs associated with managing corn rootworms in continuous maize are annually one of the largest expenditures for insect management in the United States Corn Belt. 2 Even though D. virgifera virgifera has been studied intensively for over 50 years, there is renewed interest in the biology, ecology, and genetics of this species because of its ability to rapidly adapt to management tactics, and its aggressive invasive nature. 3 This article provides a comprehensive review of D. virgifera virgifera population dynamics, specifically: diapause, larval and adult development, seasonality, spatial and temporal dynamics at local and landscape scales, invasiveness in North America and Europe, and non-trophic interactions with other arthropods. 4 Gaps in current knowledge are identified and discussed especially within the context of challenges that scientists in North America and Europe are currently facing regarding pest dynamics and the need to develop appropriate management strategies for each geographic area

Regulation of arousal and performance of a healthy non-human primate using closed-loop central thalamic deep brain stimulation

Application of closed-loop approaches in systems neuroscience and brain-computer interfaces holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation strategies to restore lost function. The anterior forebrain mesocircuit (AFM) of the mammalian brain is hypothesized to underlie arousal regulation of the cortex and striatum, and support cognitive functions during wakefulness. Dysfunction of arousal regulation is hypothesized to contribute to cognitive dysfunctions in various neurological disorders, and most prominently in patients following traumatic brain injury (TBI). Several clinical studies have explored the use of daily central thalamic deep brain stimulation (CT-DBS) within the AFM to restore consciousness and executive attention in TBI patients. In this study, we explored the use of closed-loop CT-DBS in order to episodically regulate arousal of the AFM of a healthy non-human primate (NHP) with the goal of restoring behavioral performance. We used pupillometry and near real-time analysis of ECoG signals to episodically initiate closed-loop CT-DBS and here we report on our ability to enhance arousal and restore the animal's performance. The initial computer based approach was then experimentally validated using a customized clinicalgrade DBS device, the DyNeuMo-X, a bi-directional research platform used for rapidly testing closed-loop DBS. The successful implementation of the DyNeuMo-X in a healthy NHP supports ongoing clinical trials employing the internal DyNeuMo system (NCT05437393, NCT05197816) and our goal of developing and accelerating the deployment of novel neuromodulation approaches to treat cognitive dysfunction in patients with structural brain injuries and other etiologies

High-Definition Mapping of Four Spatially Distinct Neutralizing Epitope Clusters on RiVax, a Candidate Ricin Toxin Subunit Vaccine

RiVax is a promising recombinant ricin toxin A subunit (RTA) vaccine antigen that has been shown to be safe and immunogenic in humans and effective at protecting rhesus macaques against lethal-dose aerosolized toxin exposure. We previously used a panel of RTA-specific monoclonal antibodies (MAbs) to demonstrate, by competition enzyme-linked immunosorbent assay (ELISA), that RiVax elicits similar serum antibody profiles in humans and macaques. However, the MAb binding sites on RiVax have yet to be defined. In this study, we employed hydrogen exchange-mass spectrometry (HX-MS) to localize the epitopes on RiVax recognized by nine toxin-neutralizing MAbs and one nonneutralizing MAb. Based on strong protection from hydrogen exchange, the nine MAbs grouped into four spatially distinct epitope clusters (namely, clusters I to IV). Cluster I MAbs protected RiVax's α-helix B (residues 94 to 107), a protruding immunodominant secondary structure element known to be a target of potent toxin-neutralizing antibodies. Cluster II consisted of two subclusters located on the “back side” (relative to the active site pocket) of RiVax. One subcluster involved α-helix A (residues 14 to 24) and α-helices F-G (residues 184 to 207); the other encompassed β-strand d (residues 62 to 69) and parts of α-helices D-E (154 to 164) and the intervening loop. Cluster III involved α-helices C and G on the front side of RiVax, while cluster IV formed a sash from the front to back of RiVax, spanning strands b, c, and d (residues 35 to 59). Having a high-resolution B cell epitope map of RiVax will enable the development and optimization of competitive serum profiling assays to examine vaccine-induced antibody responses across species

Impact of detergent on biophysical properties and immune response of the IpaDB fusion protein, a candidate subunit vaccine against Shigella species.

Shigella spp. are causative agents of bacillary dysentery, a human illness with high global morbidity levels, particularly among elderly and infant populations. Shigella infects via the fecal-oral route, and its virulence is dependent upon a type III secretion system (T3SS). Two components of the exposed needle tip complex of the Shigella T3SS, invasion plasmid antigen D (IpaD) and IpaB, have been identified as broadly protective antigens in the mouse lethal pneumonia model. A recombinant fusion protein (DB fusion) was created by joining the coding sequences of IpaD and IpaB. The DB fusion is coexpressed with IpaB's cognate chaperone, IpgC, for proper recombinant expression. The chaperone can then be removed by using the mild detergents octyl oligooxyethelene (OPOE) or N,N-dimethyldodecylamine N-oxide (LDAO). The DB fusion in OPOE or LDAO was used for biophysical characterization and subsequent construction of an empirical phase diagram (EPD). The EPD showed that the DB fusion in OPOE is most stable at neutral pH below 55°C. In contrast, the DB fusion in LDAO exhibited remarkable thermal plasticity, since this detergent prevents the loss of secondary and tertiary structures after thermal unfolding at 90°C, as well as preventing thermally induced aggregation. Moreover, the DB fusion in LDAO induced higher interleukin-17 secretion and provided a higher protective efficacy in a mouse challenge model than did the DB fusion in OPOE. These data indicate that LDAO might introduce plasticity to the protein, promoting thermal resilience and enhanced protective efficacy, which may be important in its use as a subunit vaccine

Severity Index for Suspected Arbovirus (SISA) : machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection

Funding: This study was supported, in part, by the Department of Defense Global Emerging Infection Surveillance (https://health.mil/Military-Health-Topics/Combat-Support/Armed-Forces-Health-Surveillance-Branch/Global-Emerging-Infections-Surveillance-and-Response) grant (P0220_13_OT) and the Department of Medicine of SUNY Upstate Medical University (http://www.upstate.edu/medicine/). D.F., M.H. and P.H. were supported by the Ben Kean Fellowship from the American Society for Tropical Medicine and Hygeine (https://www.astmh.org/awards-fellowships-medals/benjamin-h-keen-travel-fellowship-in-tropical-medi). S.J.R and A.M.S-I were supported by NSF DEB EEID 1518681, NSF DEB RAPID 1641145 (https://www.nsf.gov/), A.M.S-I was additionally supported by the Prometeo program of the National Secretary of Higher Education, Science, Technology, and Innovation of Ecuador (http://prometeo.educacionsuperior.gob.ec/).Background: Dengue, chikungunya, and Zika are arboviruses of major global health concern. Decisions regarding the clinical management of suspected arboviral infection are challenging in resource-limited settings, particularly when deciding on patient hospitalization. The objective of this study was to determine if hospitalization of individuals with suspected arboviral infections could be predicted using subject intake data. Methodology/Principal findings: Two prediction models were developed using data from a surveillance study in Machala, a city in southern coastal Ecuador with a high burden of arboviral infections. Data were obtained from subjects who presented at sentinel medical centers with suspected arboviral infection (November 2013 to September 2017). The first prediction model-called the Severity Index for Suspected Arbovirus (SISA)-used only demographic and symptom data. The second prediction model-called the Severity Index for Suspected Arbovirus with Laboratory (SISAL)-incorporated laboratory data. These models were selected by comparing the prediction ability of seven machine learning algorithms; the area under the receiver operating characteristic curve from the prediction of a test dataset was used to select the final algorithm for each model. After eliminating those with missing data, the SISA dataset had 534 subjects, and the SISAL dataset had 98 subjects. For SISA, the best prediction algorithm was the generalized boosting model, with an AUC of 0.91. For SISAL, the best prediction algorithm was the elastic net with an AUC of 0.94. A sensitivity analysis revealed that SISA and SISAL are not directly comparable to one another. Conclusions/Significance: Both SISA and SISAL were able to predict arbovirus hospitalization with a high degree of accuracy in our dataset. These algorithms will need to be tested and validated on new data from future patients. Machine learning is a powerful prediction tool and provides an excellent option for new management tools and clinical assessment of arboviral infection.Publisher PDFPeer reviewe