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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 [almost equal to]200 K, shows that the external beta fluctuations determine the temperature- and time-dependence of the passage of carbon monoxide through myoglobin, and explains the nonexponential time dependence of the protein relaxation after photodissociation.

To identify pan-ancestry and ancestry-specific loci associated with attempting suicide among veterans, we conducted a genome-wide association study (GWAS) of suicide attempts within a large, multi-ancestry cohort of U.S. veterans enrolled in the Million Veterans Program (MVP). Cases were defined as veterans with a documented history of suicide attempts in the electronic health record (EHR; N = 14,089) and controls were defined as veterans with no documented history of suicidal thoughts or behaviors in the EHR (N = 395,064). GWAS was performed separately in each ancestry group, controlling for sex, age and genetic substructure. Pan-ancestry risk loci were identified through meta-analysis and included two genome-wide significant loci on chromosomes 20 (p = 3.64 × 10−9) and 1 (p = 3.69 × 10−8). A strong pan-ancestry signal at the Dopamine Receptor D2 locus (p = 1.77 × 10−7) was also identified and subsequently replicated in a large, independent international civilian cohort (p = 7.97 × 10−4). Additionally, ancestry-specific genome-wide significant loci were also detected in African-Americans, European-Americans, Asian-Americans, and Hispanic-Americans. Pathway analyses suggested over-representation of many biological pathways with high clinical significance, including oxytocin signaling, glutamatergic synapse, cortisol synthesis and secretion, dopaminergic synapse, and circadian rhythm. These findings confirm that the genetic architecture underlying suicide attempt risk is complex and includes both pan-ancestry and ancestry-specific risk loci. Moreover, pathway analyses suggested many commonly impacted biological pathways that could inform development of improved therapeutics for suicide prevention.

In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found in the U.S. Department of Veterans Affairs, the largest integrated healthcare system in the United States, we have developed an automated, personalized risk prediction model to support the clinical decision-making process for localized prostate cancer patients. This method combines the representative power of deep learning and the analytical interpretability of parametric regression models and can implement both time-dependent and static input data. To collect a comprehensive evaluation of model performances, we calculate time-dependent C-statistics C td over 2-, 5-, and 10-year time horizons using either a composite outcome or prostate cancer mortality as the target event. The composite outcome combines the Prostate-Specific Antigen (PSA) test, metastasis, and prostate cancer mortality. Our longitudinal model Recurrent Deep Survival Machine (RDSM) achieved C td 0.85 (0.83), 0.80 (0.83), and 0.76 (0.81), while the cross-sectional model Deep Survival Machine (DSM) attained C td 0.85 (0.82), 0.80 (0.82), and 0.76 (0.79) for the 2-, 5-, and 10-year composite (mortality) outcomes, respectively. In addition to estimating the survival probability, our method can quantify the uncertainty associated with the prediction. The uncertainty scores show a consistent correlation with the prediction accuracy. We find PSA and prostate cancer stage information are the most important indicators in risk prediction. Our work demonstrates the utility of the data-driven machine learning model in prostate cancer risk prediction, which can play a critical role in the clinical decision system.

This study on severe malarial anemia (SMA: Hb < 6.0 g/dL), a leading global cause of childhood morbidity and mortality, compares the entire expressed whole blood host transcriptome between Kenyan children (3-48 mos.) with non-SMA (Hb ≥ 6.0 g/dL, n  = 39) and SMA ( n  = 18). Differential expression analyses reveal 1403 up-regulated and 279 down-regulated transcripts in SMA, signifying impairments in host inflammasome activation, cell death, and innate immune and cellular stress responses. Immune cell profiling shows decreased memory responses, antigen presentation, and immediate pathogen clearance, suggesting an immature/improperly regulated immune response in SMA. Module repertoire analysis of blood-specific gene signatures identifies up-regulation of erythroid genes, enhanced neutrophil activation, and impaired inflammatory responses in SMA. Enrichment analyses converge on disruptions in cellular homeostasis and regulatory pathways for the ubiquitin-proteasome system, autophagy, and heme metabolism. Pathway analyses highlight activation in response to hypoxic conditions [Hypoxia Inducible Factor (HIF)−1 target and Reactive Oxygen Species (ROS) signaling] as a central theme in SMA. These signaling pathways are also top-ranking in protein abundance measures and a Ugandan SMA cohort with available transcriptomic data. Targeted RNA-Seq validation shows strong concordance with our entire expressed transcriptome data. These findings identify key molecular themes in SMA pathogenesis, offering potential targets for new malaria therapies. Here, the authors analyze the blood transcriptome of Kenyan children with severe malarial anemia and observe impaired immune responses and molecular activation of hypoxia and reactive oxygen species networks, providing insight into disease pathogenesis.

Black patients were severely under-represented in the clinical trials that led to the approval of immune checkpoint inhibitors (ICIs) for all cancers. The aim of this study was to characterise the effectiveness and safety of ICIs in Black patients. We did a retrospective cohort study of patients in the US Veterans Health Administration (VHA) system's Corporate Data Warehouse containing electronic medical records for all patients who self-identified as non-Hispanic Black or African American (referred to as Black) or non-Hispanic White (referred to as White) and received PD-1, PD-L1, CTLA-4, or LAG-3 inhibitors between Jan 1, 2010, and Dec 31, 2023. Effectiveness outcomes were overall survival, time to treatment discontinuation, and time to next treatment. The safety outcome was the frequency of immune-related adverse events; assessed among a random sample of 1000 Black patients and 1000 White patients, 892 pairs were matched on the basis of baseline characteristics using 1:1 exact matching without replacement. After manual chart review, patients who did not receive ICI therapy or who had inadequate follow-up were excluded. The adjusted effect of race on each effectiveness outcome was assessed in the whole ICI-treated cohort with propensity-weighted Cox regression with robust standard errors. Immune-related adverse events outcomes were analysed in the random matched sample with multivariable Cox regression, adjusting for baseline characteristics. We identified 26 398 patients, of whom 4943 (18·7%) patients were Black, 21 455 (81·3%) were White, 895 (3·4%) were female, 25 503 (96·6%) were male, 11 859 (45%) had non-small-cell lung cancer, and 26 045 (98·7%) received PD-1 or PD-L1 inhibitors. As of data cutoff (Aug 28, 2024), median follow-up was 40·3 months (95% CI 38·3–42·3) for Black patients and 43·9 months (43·0–45·1) for White patients. Compared with White patients, Black patients had longer time to treatment discontinuation (2-year unadjusted rates 10·7% [95% CI 9·8–11·7] for Black patients vs 8·6% [8·2–9·0] for White patients; adjusted hazard ratio [HR] 0·91, 95% CI 0·87–0·95, p<0·0001), similar time to next treatment (23·5% [22·3–24·8] for Black patients vs 25·6% [25·0–26·2] for White patients; 1·00, 0·95–1·05, p=0·96), and slightly improved overall survival (36·5% [35·2–38·1] for Black patients vs 36·5% [35·8–37·1]; 0·95, 0·90–0·99, p=0·036). 1710 patients (n=862 Black and n=848 White) were analysed for safety outcomes. Compared with White patients, Black patients had a reduced risk of all-grade immune-related adverse events (unadjusted 2-year rate 33·1% [95% CI 28·9–37·1] vs 44·1% [95% CI 39·1–48·7]; adjusted HR 0·75, 95% CI 0·62–0·90, p=0·0026), immune-related adverse events requiring treatment with systemic steroids (0·61, 0·46–0·81, p=0·00051), and immune-related adverse events resulting in permanent ICI discontinuation (0·58, 0·44–0·78, p=0·00024). In exploratory analyses of irAE subtypes, a significant risk reduction in Black patients was found for colitis (0·46, 0·27–0·76, p=0·0026) and hyperthyroidism or hypothyroidism (0·63, 0·44–0·90, p=0·011), and no significant differences were found for any other immune-related adverse event subtypes analysed. Similar results were found in analyses using a steroid-based definition of immune-related adverse events among the entire ICI-treated cohort. Compared with White patients, Black patients had similar ICI effectiveness and lower toxicities among those treated in the national VHA system, potentially reflecting an important difference in the therapeutic ratio (ratio of benefit to harm) of ICIs. Our findings of decreased toxicity among Black patients require further investigation to assess their generalisability. Million Veteran Program, Office of Research and Development, Veterans Health Administration and the LUNGevity foundation.

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.

This prospective cohort study explored the association between two upstream IFN-γ variants (rs2069709: G > T and rs2069705: A > G) and hazard factors for malaria outcomes in a longitudinal cohort of children ( n  = 941, 3–36 mos.), followed for three years. The impact of age, sex, previous malaria exposure, HIV1 infection, and sickle-cell genotypes (HbAA, HbAS, and HbSS) was also investigated. Reduced malaria episodes were associated with older age at enrollment [HR = 0.957 (95% CI = 0.953–0.961) per month, P  < 2.2e-16], HIV1 infection [0.687 (0.545–0.866), P  = 0.001], being female [0.910 (0.859–0.964), P  = 0.040], and HbAS [0.823 (0.754–0.898), P  = 0.005]. The GA/TA diplotype [0.376 (0.230–0.614), P  = 0.002] also reduced the hazard of malaria, while TA haplotype increased susceptibility [1.749 (1.159–2.640), P  = 0.029]. Factors protecting against the development of SMA [Hemoglobin (Hb < 6.0 g/dL)] included older age [0.927 (0.913–0.942) per month, P  < 2.2e-16], previous malaria episodes [0.576 (0.542–0.614, P  = 9.5e-32)], HbAS [0.553 (0.400-0.766), P  = 0.015]. The rs2069705AG genotype increased the hazard of SMA [1.697 (1.002–2.875), P  = 0.042]. Reduced hazard of mortality was observed for older children [0.898 (0.857–0.941), P  < 2.2e-16], while a higher hazard was present in HIV-infected children [12.475 (6.380-24.392), P  < 2.2e-16], and in those with HbSS [6.341 (1.944–20.686), P  = 0.007]. The GG haplotype increased the mortality hazard [1.817 (0.936–3.527), P  = 0.078]. The results here highlight critical factors influencing the hazard of malaria, SMA, and mortality.

Marine cone snails are carnivorous gastropods that use peptide toxins called conopeptides both as a defense mechanism and as a means to immobilize and kill their prey. These peptide toxins exhibit a large chemical diversity that enables exquisite specificity and potency for target receptor proteins. This diversity arises in terms of variations both in amino acid sequence and length, and in posttranslational modifications, particularly the formation of multiple disulfide linkages. Most of the functionally characterized conopeptides target ion channels of animal nervous systems, which has led to research on their therapeutic applications. Many facets of the underlying molecular mechanisms responsible for the specificity and virulence of conopeptides, however, remain poorly understood. In this review, we will explore the chemical diversity of conopeptides from a computational perspective. First, we discuss current approaches used for classifying conopeptides. Next, we review different computational strategies that have been applied to understanding and predicting their structure and function, from machine learning techniques for predictive classification to docking studies and molecular dynamics simulations for molecular-level understanding. We then review recent novel computational approaches for rapid high-throughput screening and chemical design of conopeptides for particular applications. We close with an assessment of the state of the field, emphasizing important questions for future lines of inquiry.

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.

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.