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Around the world, scavenging birds such as vultures and condors have been experiencing drastic population declines. Scavenging birds have a distinct digestive process to deal with higher amounts of bacteria in their primary diet of carcasses in varying levels of decay. These observations motivate us to present an analysis of captive and healthy California condor (Gymnogyps californianus) microbiomes to characterize a population raised together under similar conditions. Shotgun metagenomic DNA sequences were analyzed from fecal and cloacal samples of captive birds. Classification of shotgun DNA sequence data with peptide signatures using the Sequedex package provided both phylogenetic and functional profiles, as well as individually annotated reads for targeted confirmatory analysis. We observed bacterial species previously associated with birds and gut microbiomes, including both virulent and opportunistic pathogens such as Clostridium perfringens, Propionibacterium acnes, Shigella flexneri, and Fusobacterium mortiferum, common flora such as Lactobacillus johnsonii, Lactobacillus ruminus, and Bacteroides vulgatus, and mucosal microbes such as Delftia acidovorans, Stenotrophomonas maltophilia, and Corynebacterium falsnii. Classification using shotgun metagenomic reads from phylogenetic marker genes was consistent with, and more specific than, analysis based on 16S rDNA data. Classification of samples based on either phylogenetic or functional profiles of genomic fragments differentiated three types of samples: fecal, mature cloacal and immature cloacal, with immature birds having approximately 40% higher diversity of microbes. [ABSTRACT FROM AUTHOR]

A common property of Cu/Zn superoxide dismutase 1 ( SOD1), harboring mutations associated with amyotrophic lateral sclerosis, is a high propensity to misfold and form abnormal aggregates. The aggregation of mutant SOD1 has been demonstrated in vitro, with purified proteins, in mouse models, in human tissues, and in cultured cell models. In vitro translation studies have determined that SOD1 with amyotrophic lateral sclerosis mutations is slower to mature, and thus perhaps vulnerable to off-pathway folding that could generate aggregates. The aggregation of mutant SOD1 in living cells can be monitored by tagging the protein with fluorescent fluorophores. In this study, we have taken advantage of the Dendra2 fluorophore technology in which excitation can be used to switch the output color from green to red, thereby clearly creating a time stamp that distinguishes pre-existing and newly made proteins. In cells that transiently over-express the Ala 4 to Val variant of SOD1-Dendra2, we observed that newly made mutant SOD1 was rapidly captured by pathologic intracellular inclusions. In cell models of mutant SOD1 aggregation over-expressing untagged A4V- SOD1, we observed that immature forms of the protein, lacking a Cu co-factor and a normal intramolecular disulfide, persist for extended periods. Our findings fit with a model in which immature forms of mutant A4V- SOD1, including newly made protein, are prone to misfolding and aggregation. [ABSTRACT FROM AUTHOR]

Biosurveillance activities focus on acquiring and analyzing epidemiological and biological data to interpret unfolding events and predict outcomes in infectious disease outbreaks. We describe a mathematical modeling framework based on geographically aligned data sources and with appropriate flexibility that partitions the modeling of disease spread into two distinct but coupled levels. A top-level stochastic simulation is defined on a network with nodes representing user-configurable geospatial "patches". Intra-patch disease spread is treated with differential equations that assume uniform mixing within the patch. We use U.S. county-level aggregated data on animal populations and parameters from the literature to simulate epidemic spread of two strikingly different animal diseases agents: foot-and-mouth disease and highly pathogenic avian influenza. Results demonstrate the capability of this framework to leverage low-fidelity data while producing meaningful output to inform biosurveillance and disease control measures. For example, we show that the possible magnitude of an outbreak is sensitive to the starting location of the outbreak, highlighting the strong geographic dependence of livestock and poultry infectious disease epidemics and the usefulness of effective biosurveillance policy. The ability to compare different diseases and host populations across the geographic landscape is important for decision support applications and for assessing the impact of surveillance, detection, and mitigation protocols. [ABSTRACT FROM AUTHOR]

FTIR difference spectroscopy is used to reveal changes in the internal structure and amino acid protonation states of bovine cytochrome c oxidase (CcO) that occur upon photolysis of the CO adduct of the two-electron reduced (mixed valence, MV) and four-electron reduced (fully reduced, FR) forms of the enzyme. FTIR difference spectra were obtained in D2O (pH 6–9.3) between the MV-CO adduct (heme a3 and CuB reduced; heme a and CuA oxidized) and a photostationary state in which the MV-CO enzyme is photodissociated under constant illumination. In the photostationary state, part of the enzyme population has heme a3 oxidized and heme a reduced. In MV-CO, the frequency of the stretch mode of CO bound to ferrous heme a3 decreases from 1965.3 cm−1 at pH* ≤7 to 1963.7 cm−1 at pH* 9.3. In the CO adduct of the fully reduced enzyme (FR-CO), the CO stretching frequency is observed at 1963.46±0.05 cm−1, independent of pH. This indicates that in MV-CO there is a group proximal to heme a that deprotonates with a pKa of about 8.3, but that remains protonated over the entire pH* range 6–9.3 in FR-CO. The pKa of this group is therefore strongly coupled to the redox state of heme a. Following photodissociation of CO from heme a3 in MV oxidases, the extent of electron transfer from heme a3 to heme a shows a pH-dependent phase between pH 7 and 9, and a pH-independent phase at all pH''s. The FTIR difference spectrum resulting from photolysis of MV-CO exhibits vibrational features of the protein backbone and side chains associated with (1) the loss of CO by the a3 heme in the absence of electron transfer, (2) the pH-independent phase of the electron transfer, and (3) the pH-dependent phase of the electron transfer. Many infrared features change intensity or frequency during both electron transfer phases and thus appear as positive or negative features in the difference spectra. In particular, a negative band at 1735 cm−1 and a positive band at 1412 cm−1 are consistent with the deprotonation of the acidic residue E242. Positive features at 1552 and 1661 cm−1 are due to amide backbone modes. Other positive and negative features between 1600 and 1700 cm−1 are consistent with redox-induced shifts in heme formyl vibrations, and the redox-linked protonation of an arginine residue, accompanying electron transfer from heme a3 to heme a. An arginine could be the residue responsible for the pH-dependent shift in the carbonyl frequency of MV-CO. Specific possibilities as to the functional significance of these observations are discussed. [Copyright &y& Elsevier]

Discusses the role of structure, energy landscape, dynamics and allostery in the enzymatic function of myoglobin. Catalytic function of myoglobin; Binding mechanisms; Localization; Myoglobin-controlled chemical reactions.

Background: The cumulative, health system‐wide survival benefit of immune checkpoint inhibitors (ICIs) is unclear, particularly among real‐world patients with limited life expectancies and among subgroups poorly represented on clinical trials. We sought to determine the health system‐wide survival impact of ICIs. Methods: We identified all patients receiving PD‐1/PD‐L1 or CTLA‐4 inhibitors from 2010 to 2023 in the national Veterans Health Administration (VHA) system (ICI cohort) and all patients who received non‐ICI systemic therapy in the years before ICI approval (historical control). ICI and historical control cohorts were matched on multiple cancer‐related prognostic factors, comorbidities, and demographics. The effect of ICI on overall survival was quantified with Cox regression incorporating matching weights. Cumulative life‐years gained system‐wide were calculated from the difference in adjusted 5‐year restricted mean survival times. Results: There were 27,322 patients in the ICI cohort and 69,801 patients in the historical control cohort. Among ICI patients, the most common cancer types were NSCLC (46%) and melanoma (10%). ICI demonstrated a large OS benefit in most cancer types with heterogeneity across cancer types (NSCLC: adjusted HR [aHR] 0.56, 95% confidence interval [CI] 0.54–0.58, p < 0.001; urothelial: aHR 0.91, 95% CI 0.83–1.01, p = 0.066). The relative benefit of ICI was stable across patient age, comorbidity, and self‐reported race subgroups. Across VHA, 15,859 life‐years gained were attributable to ICI within 5‐years of treatment, with NSCLC contributing the most life‐years gained. Conclusion: We demonstrated substantial increase in survival due to ICIs across a national health system, including in patient subgroups poorly represented on clinical trials. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) identify genetic variants underlying complex traits but are limited by stringent genome-wide significance thresholds. We present GRIN (Gene set Refinement through Interacting Networks), which increases confidence in the expanded gene set by retaining genes strongly connected by biological networks when GWAS thresholds are relaxed. GRIN was validated on both simulated interrelated gene sets as well as multiple GWAS traits. From multiple GWAS summary statistics of suicide attempt, a complex phenotype, GRIN identified additional genes that replicated across independent cohorts and retained biologically interrelated genes despite a relaxed significance threshold. We present a conceptual model of how these retained genes interact through neurobiological pathways that may influence suicidal behavior, and identify existing drugs associated with these pathways that would not have been identified under traditional GWAS thresholds. We demonstrate GRIN's utility in boosting GWAS results by increasing the number of true positive genes identified from GWAS results. Using the software GRIN, GWAS results are refined by reducing false positive genes using biological network topology, allowing users to lower GWAS significance thresholds to identify additional genes associated with complex traits [ABSTRACT FROM AUTHOR]

We present a microscopic model of carbon monoxide (CO) binding to myoglobin which reproduces the experimentally observed Arrhenius pre-exponential factor of 10[sup 9] s[sup -1] and activation enthalpy distribution centered at 12 kJ/mol. The model is based on extensive ab initio calculations of CO interacting with a model heme-imidazole group which we performed using a fully quantum mechanical Hartree-Fock/density functional theory (HF/DFT) hybrid method. We fit the HF/DFT calculated energies, obtained for over 1000 heme-CO structures with varied CO and iron positions and orientations for both high (S=2) and low (S=0) spin states, to a model potential function which includes a bonding interaction in both of the spin states, electrostatic, and anisotropic Lennard-Jones-type interactions. By combining the x-ray determined protein structure with this potential and protein-CO interactions and internal heme interaction potentials obtained from established molecular dynamics literature, we calculate the energy required for the CO to reach the spin crossing from the heme pocket. We find that the transition between the two spin states occurs when CO and iron have activation enthalpies of 8 kJ/mol and 3 kJ/mol, respectively, which are necessary to move CO towards the iron and the iron atom relative to the heme plane N[sub pyr]. At the same time we find that 1 kJ/mol is needed to move N[sub ε] of His-64 and C[sub γ] of Val-68 relative to the heme group. The requirement that these motions be synchronized reduces the Arrhenius pre-exponential by a factor of 150 from the 10[sup 12] s[sup -1] obtained from CO motion across the heme pocket, leaving a factor of ∼ 6 to account for CO orientation and nonadiabaticity of the electronic spin change. The observed width of the enthalpy distribution is reproduced by assuming a Gaussian distribution of the heme positions with a standard deviation of 0.2 Å. We characterize the conformational relaxation by cal... [ABSTRACT FROM AUTHOR]

Antipsychotic drugs are widely used to treat psychiatric disorders such as schizophrenia and bipolar disorder. However, they are known to have important adverse effects related to metabolic syndrome (MS), which significantly impacts their effectiveness. Quantifying this effect is complicated because the temporal patterns of both antipsychotic medication usage and MS-related outcomes vary across patients. To minimize deleterious effects on patients, it is of the utmost importance to characterize and predict MS effects using genetic and non-genetic information available in electronic medical record (EmR) datasets. For our initial analysis, we aggregate patient's EMR data into three-month intervals, and perform model selection and linear regression to identify important covariates such as age, sex, comorbid medical conditions, oral hypoglycemic, statin, and metformin usage, and hospitalization. We further develop a longitudinal deep learning model that uses high dimensional EMR data to predict MSvar trajectories and utilize SHAP (Shapely additive explanations) values to identify both temporal and polypharmacy-related patterns, predictive of adverse metabolic effects. In addition, we utilize highly predictive phenotypes developed on the full cohort of VA patients to perform GWAS for the rate of change in MS outcomes as a function of SNP genotype in the MVP population (currently 658,582 patients with genomic profiles) and antipsychotic type, while adjusting for 20 ancestry principal components and other biologically relevant covariates. Subsequently, we use summary statistics from GWAS to perform transcriptome/proteome/methylome wide analyses, at both gene and gene set levels, employing state-of-the-art mendelian randomization tools. Our cohort was selected on the basis of antipsychotic medication usage and completeness of the characterization of our MS outcomes, from all 25 million patients with records in the VA EMR system, in use between 2000 to 2023. This results in a combined 12,303,200 patient-quarters of observation among 135,200 unique patients who have had at least one hundred measurements of weight and ten antipsychotic prescription fills. We find in a multivariate logistic regression that Quetiapine, Risperidone, Olanzapine, and Aripiprazole were associated with weight gain and Haloperidol, Ziprasidone, and Fluphenazine with weight loss in quarters (three month periods) when each medication was prescribed at least once, compared to quarters without the medication. This was when controlling for Anxiety and Bipolar diagnoses (associated with weight gain), as well as diabetes medications (metformin associated with weight loss, while insulin with weight gain) and statins, which were associated with weight gain. We further observed a strong impact of age on these associations, with diabetes medications and statins relatively more important in older patients and the antipsychotic medications with the young. GWAS results will be presented. Several second-generation antipsychotics are associated with weight gain, while two first-generation antipsychotics, as well as ziprasidone, are associated with weight loss. These are age-dependent and impacted by both psychiatric diagnoses as well as insulin, statin, and metformin use. GWAS results will be discussed. [ABSTRACT FROM AUTHOR]

Background: Emerging pathogens such as Zika, chikungunya, Ebola, and dengue viruses are serious threats to national and global health security. Accurate forecasts of emerging epidemics and their severity are critical to minimizing subsequent mortality, morbidity, and economic loss. The recent introduction of chikungunya and Zika virus to the Americas underscores the need for better methods for disease surveillance and forecasting.Methods: To explore the suitability of current approaches to forecasting emerging diseases, the Defense Advanced Research Projects Agency (DARPA) launched the 2014-2015 DARPA Chikungunya Challenge to forecast the number of cases and spread of chikungunya disease in the Americas. Challenge participants (n=38 during final evaluation) provided predictions of chikungunya epidemics across the Americas for a six-month period, from September 1, 2014 to February 16, 2015, to be evaluated by comparison with incidence data reported to the Pan American Health Organization (PAHO). This manuscript presents an overview of the challenge and a summary of the approaches used by the winners.Results: Participant submissions were evaluated by a team of non-competing government subject matter experts based on numerical accuracy and methodology. Although this manuscript does not include in-depth analyses of the results, cursory analyses suggest that simpler models appear to outperform more complex approaches that included, for example, demographic information and transportation dynamics, due to the reporting biases, which can be implicitly captured in statistical models. Mosquito-dynamics, population specific information, and dengue-specific information correlated best with prediction accuracy.Conclusion: We conclude that with careful consideration and understanding of the relative advantages and disadvantages of particular methods, implementation of an effective prediction system is feasible. However, there is a need to improve the quality of the data in order to more accurately predict the course of epidemics. [ABSTRACT FROM AUTHOR]

Background: Plasmodium falciparum malaria is a leading cause of pediatric morbidity and mortality in holoendemic transmission areas. Severe malarial anemia [SMA, hemoglobin (Hb) < 5.0 g/dL in children] is the most common clinical manifestation of severe malaria in such regions. Although innate immune response genes are known to influence the development of SMA, the role of natural killer (NK) cells in malaria pathogenesis remains largely undefined. As such, we examined the impact of genetic variation in the gene encoding a primary NK cell receptor, natural cytotoxicity-triggering receptor 3 (NCR3), on the occurrence of malaria and SMA episodes over time. Methods: Susceptibility to malaria, SMA, and all-cause mortality was determined in carriers of NCR3 genetic variants (i.e., rs2736191:C > G and rs11575837:C > T) and their haplotypes. The prospective observational study was conducted over a 36 mos. follow-up period in a cohort of children (n = 1,515, aged 1.9–40 mos.) residing in a holoendemic P. falciparum transmission region, Siaya, Kenya. Results: Poisson regression modeling, controlling for anemia-promoting covariates, revealed a significantly increased risk of malaria in carriers of the homozygous mutant allele genotype (TT) for rs11575837 after multiple test correction [Incidence rate ratio (IRR) = 1.540, 95% CI = 1.114–2.129, P = 0.009]. Increased risk of SMA was observed for rs2736191 in children who inherited the CG genotype (IRR = 1.269, 95% CI = 1.009–1.597, P = 0.041) and in the additive model (presence of 1 or 2 copies) (IRR = 1.198, 95% CI = 1.030–1.393, P = 0.019), but was not significant after multiple test correction. Modeling of the haplotypes revealed that the CC haplotype had a significant additive effect for protection against SMA (i.e., reduced risk for development of SMA) after multiple test correction (IRR = 0.823, 95% CI = 0.711–0.952, P = 0.009). Although increased susceptibility to SMA was present in carriers of the GC haplotype (IRR = 1.276, 95% CI = 1.030–1.581, P = 0.026) with an additive effect (IRR = 1.182, 95% CI = 1.018–1.372, P = 0.029), the results did not remain significant after multiple test correction. None of the NCR3 genotypes or haplotypes were associated with all-cause mortality. Conclusions: Variation in NCR3 alters susceptibility to malaria and SMA during the acquisition of naturally-acquired malarial immunity. These results highlight the importance of NK cells in the innate immune response to malaria. [ABSTRACT FROM AUTHOR]

Suicidal ideation (SI) often precedes and predicts suicide attempt and death, is the most common suicidal phenotype and is over-represented in veterans. The genetic architecture of SI in the absence of suicide attempt (SA) is unknown, yet believed to have distinct and overlapping risk with other suicidal behaviors. We performed the first GWAS of SI without SA in the Million Veteran Program (MVP), identifying 99,814 SI cases from electronic health records without a history of SA or suicide death (SD) and 512,567 controls without SI, SA or SD. GWAS was performed separately in the four largest ancestry groups, controlling for sex, age and genetic substructure. Ancestry-specific results were combined via meta-analysis to identify pan-ancestry loci. Four genome-wide significant (GWS) loci were identified in the pan-ancestry meta-analysis with loci on chromosomes 6 and 9 associated with suicide attempt in an independent sample. Pan-ancestry gene-based analysis identified GWS associations with DRD2, DCC, FBXL19, BCL7C, CTF1, ANNK1, and EXD3. Gene-set analysis implicated synaptic and startle response pathways (q's<0.05). European ancestry (EA) analysis identified GWS loci on chromosomes 6 and 9, as well as GWS gene associations in EXD3, DRD2, and DCC. No other ancestry-specific GWS results were identified, underscoring the need to increase representation of diverse individuals. The genetic correlation of SI and SA within MVP was high (rG = 0.87; p = 1.09e-50), as well as with post-traumatic stress disorder (PTSD; rG = 0.78; p = 1.98e-95) and major depressive disorder (MDD; rG = 0.78; p = 8.33e-83). Conditional analysis on PTSD and MDD attenuated most pan-ancestry and EA GWS signals for SI without SA to nominal significance, with the exception of EXD3 which remained GWS. Our novel findings support a polygenic and complex architecture for SI without SA which is largely shared with SA and overlaps with psychiatric conditions frequently comorbid with suicidal behaviors. Author summary: Suicidal ideation often precedes and predicts suicide attempt and death, is the most common suicidal phenotype and is over-represented in veterans. The underlying genetic risk of suicidal ideation in the absence of suicide attempt is unknown, yet believed to have distinct and overlapping risk with other suicidal behaviors. To find these potential genetic risk variants, we examined blood samples from over 600,000 veterans from Million Veteran Program. Four regions of the genome were significantly associated with risk of suicidal ideation in the absence of suicide attempt across four major ancestral groups (European, African, Hispanic and Asian). Two of those regions on chromosomes 6 and 9, were also significantly associated with suicidal ideation in European ancestry-only veterans, as well as with suicide attempt in an independent sample of mostly civilian individuals. No other genetic regions were identified in other ancestry-only analyses, underscoring the need to increase representation of individuals with diverse ancestry. Suicidal ideation without suicide attempt was genetically correlated with other psychiatric disorders, including suicide attempt, post-traumatic stress disorder and major depressive disorder. Our novel findings suggest that multiple genes contribute to genetic risk for suicidal ideation and that the genetic risk is largely shared with suicide attempt and other psychiatric conditions that frequently co-occur with suicidal ideation. [ABSTRACT FROM AUTHOR]

Suicide is a major public health problem affecting US Veterans and the US in general. Many variables (e.g., demographic, clinical, biological, geographic) have been associated with risk for suicide and suicidal behavior, including altitude; however, the exact nature of the relationship between altitude and suicide remains unclear in part due to the fact that previous studies have used either geospatial data or individual-level data, but not both. Prior research has also failed to consider the full range of suicidal thoughts and behaviors, ranging from suicidal ideation to suicide deaths. Accordingly, the objective of the present research was to use both geospatial data (county and zip codes) and individual-level data to comprehensively assess the association between altitude and suicide mortality, suicide attempts, and suicidal ideation among US Veterans between 2000 and 2018. Taken together, our results demonstrate that there is a strong correlation between altitude and suicide rates at all the levels investigated and using different statistical analyses and even after controlling for significant covariates such as percent of age >50yr, percent male, percent white, percent non-Hispanic, median household income, and population density. We show that there is a positive correlation between altitude and suicide attempts especially when controlling by the covariates and a weak correlation between altitude and suicide ideation and the combination of suicide, suicide attempts and suicide ideation. • There is a significant correlation between altitude and suicide mortality rate for U.S. veterans. • The association between altitude and suicide deaths is higher than the association with suicide attempts or ideation. • Controlling for relevant factors improves the correlation. [ABSTRACT FROM AUTHOR]

Determining optimal surveillance networks for an emerging pathogen is difficult since it is not known beforehand what the characteristics of a pathogen will be or where it will emerge. The resources for surveillance of infectious diseases in animals and wildlife are often limited and mathematical modeling can play a supporting role in examining a wide range of scenarios of pathogen spread. We demonstrate how a hierarchy of mathematical and statistical tools can be used in surveillance planning help guide successful surveillance and mitigation policies for a wide range of zoonotic pathogens. The model forecasts can help clarify the complexities of potential scenarios, and optimize biosurveillance programs for rapidly detecting infectious diseases. Using the highly pathogenic zoonotic H5N1 avian influenza 2006-2007 epidemic in Nigeria as an example, we determined the risk for infection for localized areas in an outbreak and designed biosurveillance stations that are effective for different pathogen strains and a range of possible outbreak locations. We created a general multi-scale, multi-host stochastic SEIR epidemiological network model, with both short and long-range movement, to simulate the spread of an infectious disease through Nigerian human, poultry, backyard duck, and wild bird populations. We chose parameter ranges specific to avian influenza (but not to a particular strain) and used a Latin hypercube sample experimental design to investigate epidemic predictions in a thousand simulations. We ranked the risk of local regions by the number of times they became infected in the ensemble of simulations. These spatial statistics were then complied into a potential risk map of infection. Finally, we validated the results with a known outbreak, using spatial analysis of all the simulation runs to show the progression matched closely with the observed location of the farms infected in the 2006-2007 epidemic. [ABSTRACT FROM AUTHOR]

Plasmodium falciparum infections remain among the leading causes of morbidity and mortality in holoendemic transmission areas. Located within region 5q31.1, the colony-stimulating factor 2 gene (CSF2) encodes granulocyte–macrophage colony-stimulating factor (GM-CSF), a hematopoietic growth factor that mediates host immune responses. Since the effect of CSF2 variation on malaria pathogenesis remains unreported, we investigated the impact of two genetic variants in the 5q31.1 gene region flanking CSF2:g-7032 G > A (rs168681:G > A) and CSF2:g.64544T > C (rs246835:T > C) on the rate and timing of malaria and severe malarial anemia (SMA, Hb < 5.0 g/dL) episodes over 36 months of follow-up. Children (n = 1654, aged 2–70 months) were recruited from a holoendemic P. falciparum transmission area of western Kenya. Decreased incidence rate ratio (IRR) for malaria was conferred by inheritance of the CSF2:g.64544 TC genotype (P = 0.0277) and CSF2 AC/GC diplotype (P = 0.0015). Increased IRR for malaria was observed in carriers of the CSF2 AT/GC diplotype (P = 0.0237), while the inheritance of the CSF2 AT haplotype increased the IRR for SMA (P = 0.0166). A model estimating the longitudinal risk of malaria showed decreased hazard rates among CSF2 AC haplotype carriers (P = 0.0045). Investigation of all-cause mortality revealed that inheritance of the GA genotype at CSF2:g-7032 increased the risk of mortality (P = 0.0315). Higher risk of SMA and all-cause mortality were observed in younger children (P < 0.0001 and P = 0.0015), HIV-1(+) individuals (P < 0.0001 and P < 0.0001), and carriers of HbSS (P = 0.0342 and P = 0.0019). Results from this holoendemic P. falciparum area show that variation in gene region 5q31.1 influences susceptibility to malaria, SMA, and mortality, as does age, HIV-1 status, and inheritance of HbSS. [ABSTRACT FROM AUTHOR]

Many mathematical models have been proposed to predict death following the Coronavirus Disease 2019 (COVID-19); all started with comorbidity subsets for this still-little understood disease. Thus, we derived a novel predicted probability of death model (PDeathDx) upon all diagnostic codes documented in the Department of Veterans Affairs. We present the conceptual underpinnings and analytic approach in estimating the independent contribution of pre-existing conditions. This is the largest study to-date following patients with COVID-19 to predict mortality. Cases were identified with at least one positive nucleic acid amplification test. Starting in 1997, we use diagnoses from the first time a patient sought care until 14 days before a positive nucleic acid amplification test. We demonstrate the clear advantage of using an unrestricted set of pre-existing conditions to model COVID-19 mortality, as models using conventional comorbidity indices often assign little weight or usually do not include some of the highest risk conditions; the same is true of conditions associated with COVID-19 severity. Our findings suggest that it is risky to pick comorbidities for analysis without a systematic review of all those experienced by the cohort. Unlike conventional approaches, our comprehensive methodology provides the flexibility that has been advocated for comorbidity indices since 1993; such an approach can be readily adapted for other diseases and outcomes. With our comorbidity risk adjustment approach outperforming conventional indices for predicting COVID-19 mortality, it shows promise for predicting outcomes for other conditions of interest. [ABSTRACT FROM AUTHOR]

Non-typhoidal Salmonella (NTS) is a major global health concern that often causes bloodstream infections in areas of the world affected by malnutrition and comorbidities such as HIV and malaria. Developing a strategy to control the emergence and spread of highly invasive and antimicrobial resistant NTS isolates requires a comprehensive analysis of epidemiological factors and molecular pathogenesis. Here, we characterize 11 NTS isolates that caused bloodstream infections in pediatric patients in Siaya, Kenya from 2003–2010. Nine isolates were identified as S. Typhimurium sequence type 313 while the other two were S. Enteritidis. Comprehensive genotypic and phenotypic analyses were performed to compare these isolates to those previously identified in sub-Saharan Africa. We identified a S. Typhimurium isolate referred to as UGA14 that displayed novel plasmid, pseudogene and resistance features as compared to other isolates reported from Africa. Notably, UGA14 is able to ferment both lactose and sucrose due to the acquisition of insertion elements on the pKST313 plasmid. These findings show for the first time the co-evolution of plasmid-mediated lactose and sucrose metabolism along with cephalosporin resistance in NTS further elucidating the evolutionary mechanisms of invasive NTS phenotypes. These results further support the use of combined genomic and phenotypic approaches to detect and characterize atypical NTS isolates in order to advance biosurveillance efforts that inform countermeasures aimed at controlling invasive and antimicrobial resistant NTS. Author summary: Non-typhoidal Salmonella (NTS) has been associated with life-threatening bacteremia in sub-Saharan Africa where co-morbidities such as HIV and malaria are highly prevalent. Children under the age of 5 are especially vulnerable to invasive NTS infections. The emergence and spread of multi-drug resistant invasive NTS isolates have limited the availability of effective treatment options. Understanding the molecular mechanisms that drive the evolution of invasive and antimicrobial resistant NTS strains is key to mitigating their impact on human health. In this study, we obtained 11 NTS isolates from the bloodstreams of children in Siaya, Kenya and performed both genotypic and phenotypic characterization compared to antimicrobial sensitive NTS strains. One strain, named UGA14, displayed a unique plasmid makeup compared to the other 10 isolates, which encoded for cephalosporin resistance as well as novel metabolic features allowing it to metabolize both lactose and sucrose. Not only was UGA14 multi-drug resistant but its unique metabolic profile made it indistinguishable from Escherichia coli on brilliant green agar indicating the failure of traditional culture-based techniques to inform diagnose and treatment decisions. These findings highlight the importance of comparative genotypic and phenotypic analyses to understand the driving mechanisms of invasive and drug-resistant pathogens and support the development of effective countermeasures. [ABSTRACT FROM AUTHOR]

Viral proteins evade host immune function by molecular mimicry, often achieved by short linear motifs (SLiMs) of three to ten consecutive amino acids (AAs). Motif mimicry tolerates mutations, evolves quickly to modify interactions with the host, and enables modular interactions with protein complexes. Host cells cannot easily coordinate changes to conserved motif recognition and binding interfaces under selective pressure to maintain critical signaling pathways. SLiMs offer potential for use in synthetic biology, such as better immunogens and therapies, but may also present biosecurity challenges. We survey viral uses of SLiMs to mimic host proteins, and information resources available for motif discovery. As the number of examples continues to grow, knowledge management tools are essential to help organize and compare new findings. • Short linear motifs (SLiMs) are patterns of three to ten consecutive AAs used by eukaryotic cells for tasks that include: signaling, localization, degradation, and proteolytic cleavage. • Viruses use SLiMs to their advantage, including interference with antiviral innate immune pathways. • Viral SLiMs can tolerate mutations, evolve quickly to modify host interactions, and co-occur in a modular manner or involve multiprotein complexes. • SLiMs are useful in synthetic biology, where minor edits can alter target specificity, modulate persistence, reprogram interactions with cell-signaling domains, and alter protein function in myriad other ways. • Aside from possible beneficial uses, for example, to produce better immunogens and develop therapeutic interventions against infectious disease, SLiMs may help characterize new and emerging threats to global health. [ABSTRACT FROM AUTHOR]

Bacteremia and malaria coinfection is a common and life-threatening condition in children residing in sub-Saharan Africa. We previously showed that coinfection with Gram negative (G[−]) enteric Bacilli and Plasmodium falciparum (Pf[+]) was associated with reduced high-density parasitemia (HDP, >10,000 parasites/μL), enhanced respiratory distress, and severe anemia. Since inflammatory mediators are largely unexplored in such coinfections, circulating cytokines were determined in four groups of children (n=206, aged <3 yrs): healthy; Pf[+] alone; G[−] coinfected; and G[+] coinfected. Staphylococcus aureus and non-Typhi Salmonella were the most frequently isolated G[+] and G[−] organisms, respectively. Coinfected children, particularly those with G[−] pathogens, had lower parasite burden (peripheral and geometric mean parasitemia and HDP). In addition, both coinfected groups had increased IL-4, IL-5, IL-7, IL-12, IL-15, IL-17, IFN-γ, and IFN-α and decreased TNF-α relative to malaria alone. Children with G[−] coinfection had higher IL-1β and IL-1Ra and lower IL-10 than the Pf[+] group and higher IFN-γ than the G[+] group. To determine how the immune response to malaria regulates parasitemia, cytokine production was investigated with a multiple mediation model. Cytokines with the greatest mediational impact on parasitemia were IL-4, IL-10, IL-12, and IFN-γ. Results here suggest that enhanced immune activation, especially in G[−] coinfected children, acts to reduce malaria parasite burden. [ABSTRACT FROM AUTHOR]

Highlights: [•] The Lamb–Mössbauer factor is problematic in proteins. [•] The protein dynamical transition is predicted by T-dependent hydration kinetics. [•] The protein glass transition is consistent with T-dependent hydration kinetics. [•] The dynamical crossover in proteins is highly improbable. [ABSTRACT FROM AUTHOR]

Background: Classification is difficult for shotgun metagenomics data from environments such as soils, where the diversity of sequences is high and where reference sequences from close relatives may not exist. Approaches based on sequence-similarity scores must deal with the confounding effects that inheritance and functional pressures exert on the relation between scores and phylogenetic distance, while approaches based on sequence alignment and tree-building are typically limited to a small fraction of gene families. We describe an approach based on finding one or more exact matches between a read and a precomputed set of peptide 10-mers. Results: At even the largest phylogenetic distances, thousands of 10-mer peptide exact matches can be found between pairs of bacterial genomes. Genes that share one or more peptide 10-mers typically have high reciprocal BLAST scores. Among a set of 403 representative bacterial genomes, some 20 million 10-mer peptides were found to be shared. We assign each of these peptides as a signature of a particular node in a phylogenetic reference tree based on the RNA polymerase genes. We classify the phylogeny of a genomic fragment (e.g., read) at the most specific node on the reference tree that is consistent with the phylogeny of observed signature peptides it contains. Using both synthetic data from four newly-sequenced soil-bacterium genomes and ten real soil metagenomics data sets, we demonstrate a sensitivity and specificity comparable to that of the MEGAN metagenomics analysis package using BLASTX against the NR database. Phylogenetic and functional similarity metrics applied to real metagenomics data indicates a signal-to-noise ratio of approximately 400 for distinguishing among environments. Our method assigns ~6.6 Gbp/hr on a single CPU, compared with 25 kbp/hr for methods based on BLASTX against the NR database. Conclusions: Classification by exact matching against a precomputed list of signature peptides provides comparable results to existing techniques for reads longer than about 300 bp and does not degrade severely with shorter reads. Orders of magnitude faster than existing methods, the approach is suitable now for inclusion in analysis pipelines and appears to be extensible in several different directions. [ABSTRACT FROM AUTHOR]

Protein functions require conformational motions. We show here that the dominant conformational motions are slaved by the hydration shell and the bulk solvent. The protein contributes the structure necessary for function. We formulate a model that is based on experiments, insights from the physics of glass-forming liquids, and the concepts of a hierarchically organized energy landscape. To explore the effect of external fluctuations on protein dynamics, we measure the fluctuations in the bulk solvent and the hydration shell with broadband dielectric spectroscopy and compare them with internal fluctuations measured with the Mössbauer effect and neutron scattering. The result is clear. Large-scale protein motions are slaved to the fluctuations in the bulk solvent. They are controlled by the solvent viscosity, and are absent in a solid environment, Internal protein motions are slaved to the beta fluctuations of the hydration shell, are controlled by hydration, and are absent in a dehydrated protein. The model quantitatively predicts the rapid increase of the mean-square displacement above ≈200 K, shows that the external beta fluctuations determine the temperatureand time-dependence of the passage of carbon monoxide through myoglobin, and explains the nonexponential time dependence of the protein relaxation after photodissociation. [ABSTRACT FROM AUTHOR]

Atomic motions and energetics for a phosphate transfer reaction catalyzed by the cAMP-dependent protein kinase are calculated by plane-wave density functional theory, starting from structures of proteins crystallized in both the reactant conformation (RC) and the transition-state conformation (TC). In TC, we calculate that the reactants and products are nearly isoenergetic with a 20-kJ/mol barrier, whereas phosphate transfer is unfavorable by 120 kJ/mol in the RC, with an even higher barrier. With the protein in TC, the motions involved in reaction are small, with only Pγ and the catalytic proton moving >0.5 Å. Examination of the structures reveals that in the RC the active site cleft is not completely closed and there is insufficient space for the phosphorylated serine residue in the product state. Together, these observations imply that the phosphate transfer reaction occurs rapidly and reversibly in a particular conformation of the protein, and that the reaction can be gated by changes of a few tenths of an angstrom in the catalytic site. [ABSTRACT FROM AUTHOR]

Seasonal flu is an acute respiratory disease that exacts a massive toll on human populations, healthcare systems and economies. The disease is caused by an enveloped Influenza virus containing eight ribonucleoprotein (RNP) complexes. Each RNP incorporates multiple copies of nucleoprotein (NP), a fragment of the viral genome (vRNA), and a viral RNA-dependent RNA polymerase (POL), and is responsible for packaging the viral genome and performing critical functions including replication and transcription. A complete model of an Influenza RNP in atomic detail can elucidate the structural basis for viral genome functions, and identify potential targets for viral therapeutics. In this work we construct a model of a complete Influenza A RNP complex in atomic detail using multiple sources of structural and sequence information and a series of homology-modeling techniques, including a motif-matching fragment assembly method. Our final model provides a rationale for experimentally-observed changes to viral polymerase activity in numerous mutational assays. Further, our model reveals specific interactions between the three primary structural components of the RNP, including potential targets for blocking POL-binding to the NP-vRNA complex. The methods developed in this work open the possibility of elucidating other functionally-relevant atomic-scale interactions in additional RNP structures and other biomolecular complexes. [ABSTRACT FROM AUTHOR]

While astrocytes, the most abundant cells found in the brain, have many diverse functions, their role in the lysosomal storage disorder Gaucher disease (GD) has not been explored. GD, resulting from the inherited deficiency of the enzyme glucocerebrosidase and subsequent accumulation of glucosylceramide and its acylated derivative glucosylsphingosine, has both non-neuronopathic (GD1) and neuronopathic forms (GD2 and 3). Furthermore, mutations in GBA1, the gene mutated in GD, are an important risk factor for Parkinson's disease (PD). To elucidate the role of astrocytes in the disease pathogenesis, we generated iAstrocytes from induced pluripotent stem cells made from fibroblasts taken from controls and patients with GD1, with and without PD. We also made iAstrocytes from an infant with GD2, the most severe and progressive form, manifesting in infancy. Gaucher iAstrocytes appropriately showed deficient glucocerebrosidase activity and levels and substrate accumulation. These cells exhibited varying degrees of astrogliosis, Glial Fibrillary Acidic Protein (GFAP) up-regulation and cellular proliferation, depending on the level of residual glucocerebrosidase activity. Glutamte uptake assays demonstrated that the cells were functionally active, although the glutamine transporter EEAT2 was upregulated and EEAT1 downregulated in the GD2 samples. GD2 iAstrocytes were morphologically different, with severe cytoskeletal hypertrophy, overlapping of astrocyte processes, pronounced up-regulation of GFAP and S100β, and significant astrocyte proliferation, recapitulating the neuropathology observed in patients with GD2. Although astrocytes do not express α-synuclein, when the iAstrocytes were co-cultured with dopaminergic neurons generated from the same iPSC lines, excessive α-synuclein released from neurons was endocytosed by astrocytes, translocating into lysosomes. Levels of aggregated α-synuclein increased significantly when cells were treated with monomeric or fibrillar α-synuclein. GD1-PD and GD2 iAstrocytes also exhibited impaired Cathepsin D activity, leading to further α-synuclein accumulation. Cytokine and chemokine profiling of the iAstrocytes demonstrated an inflammatory response. Thus, in patients with GBA1 -associated parkinsonism, astrocytes appear to play a role in α-synuclein accumulation and processing, contributing to neuroinflammation. • iPS-derived astrocytes were generated from patients with Gaucher disease (GD). • GD iAstrocytes show low glucerebrosidase activity and accumulate glucoslyceramide. • Type 2 GD iAstrocytes exhibit upregulated GFAP and severe diffuse astrogliosis. • Excessive α-synuclein from neurons is taken up by astrocytes and moved into lysosomes. • Cathepsin D activity is low in iAstrocytes from Parkinson's disease and GD2 patients. [ABSTRACT FROM AUTHOR]

Membrane fusion proteins are responsible for viral entry into host cells—a crucial first step in viral infection. These proteins undergo large conformational changes from pre-fusion to fusion-initiation structures, and, despite differences in viral genomes and disease etiology, many fusion proteins are arranged as trimers. Structural information for both pre-fusion and fusion-initiation states is critical for understanding virus neutralization by the host immune system. In the case of Ebola virus glycoprotein (EBOV GP) and Zika virus envelope protein (ZIKV E), pre-fusion state structures have been identified experimentally, but only partial structures of fusion-initiation states have been described. While the fusion-initiation structure is in an energetically unfavorable state that is difficult to solve experimentally, the existing structural information combined with computational approaches enabled the modeling of fusion-initiation state structures of both proteins. These structural models provide an improved understanding of four different neutralizing antibodies in the prevention of viral host entry. [ABSTRACT FROM AUTHOR]