Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist

Performance assessment of a new serological diagnostic test for COVID-19 with candidate peptides from spike and nucleocapsid viral proteins

Numerous commercial tests for the serological diagnosis of COVID-19 have been produced in recent years. However, it is important to note that these tests exhibit significant variability in their sensitivity, specificity, and accuracy of results. Therefore, the objective of this study was to utilize bioinformatics tools to map SARS-CoV-2 peptides, with the goal of developing a new serological diagnostic test for COVID-19. Two peptides from the S protein and one from the N protein were selected and characterized in silico, chemically synthesized, and used as a serological diagnostic tool to detect IgM, IgG, and IgA anti-SARS-CoV-2 antibodies through the ELISA technique, confirmed as positive and negative samples by RT-qPCR or serology by ELISA. The results showed a sensitivity, specificity, Positive Predictive Value and Negative Predictive Value of 100% (p < 00001, 95% CI) for the proposed test. Although preliminary, this study brings proof-of-concept results that are consistent with the high-performance rates of the ELISA test when compared to other well-established methods for diagnosing COVID-19.

Fine-scale spatial and social patterns of SARS-CoV-2 transmission from identical pathogen sequences

Pathogen genomics can provide insights into disease transmission patterns, but new methods are needed to handle modern large-scale pathogen genome datasets. Genetically proximal viruses indicate epidemiological linkage and are informative about transmission events. Here, we leverage pairs of identical sequences using 114,298 SARS-CoV-2 genomes collected via sentinel surveillance from March 2021 to December 2022 in Washington State, USA, with linked age and residence information to characterize fine-scale transmission. The location of pairs of identical sequences is highly consistent with expectations from mobility and social contact data. Outliers in the relationship between genetic and mobility data can be explained by SARS-CoV-2 transmission between postal codes with male prisons, consistent with transmission between prison facilities. Transmission patterns between age groups vary across spatial scales. Finally, we use the timing of sequence collection to understand the age groups driving transmission. This work improves our ability to characterize transmission from large pathogen genome datasets.

Predicting the spatial expansion of an animal population with presence-only data

Predictive models can improve the efficiency of wildlife management by guiding actions at the local, landscape and regional scales. In recent decades, a vast range of modelling techniques have been developed to predict species distributions and patterns of population spread. However, data limitations often constrain the precision and biological realism of models, which make them less useful for supporting decision-making. Complex models can also be challenging to evaluate, and the results are often difficult to interpret for wildlife management practitioners. There is therefore a need to develop techniques that are appropriately robust, but also accessible to a range of end users. We developed a hybrid species distribution model that utilises commonly available presence-only distribution data and minimal demographic information to predict the spread of roe deer (Capreolus caprelous) in Great Britain. We take a novel approach to representing the environment in the model by constraining the size of habitat patches to the home-range area of an individual. Population dynamics are then simplified to a set of generic rules describing patch occupancy. The model is constructed and evaluated using data from a populated region (England and Scotland) and applied to predict regional-scale patterns of spread in a novel region (Wales). It is used to forecast the relative timing of colonisation events and identify important areas for targeted surveillance and management. The study demonstrates the utility of presence-only data for predicting the spread of animal species and describes a method of reducing model complexity while retaining important environmental detail and biological realism. Our modelling approach provides a much-needed opportunity for users without specialist expertise in computer coding to leverage limited data and make robust, easily interpretable predictions of spread to inform proactive population management.

Modeling the impact of national and regional lockdowns on the 2020 spring wave of COVID-19 in France

Several countries have implemented lockdowns to control their COVID-19 epidemic. However, questions like "where" and "when" still require answers. We assessed the impact of national and regional lockdowns considering the French first epidemic wave of COVID-19 as a case study. In a regional lockdown scenario aimed at preventing intensive care units (ICU) saturation, almost all French regions would have had to implement a lockdown within 10 days and 96% of ICU capacities would have been used. For slowly growing epidemics, with a lower reproduction number, the expected delays between regional lockdowns increase. However, the public health costs associated with these delays tend to grow with time. In a quickly growing pandemic wave, defining the timing of lockdowns at a regional rather than national level delays by a few days the implementation of a nationwide lockdown but leads to substantially higher morbidity, mortality, and stress on the healthcare system.

Evolutionary dynamics of Usutu virus: Worldwide dispersal patterns and transmission dynamics in Europe

Background

Usutu virus (USUV) is an emerging mosquito-borne Flavivirus, with birds as the main zoonotic reservoir. Humans are accidental hosts and mostly develop mild or even asymptomatic infections, although severe complications such as encephalitis can also arise. Detailed characterization of the pathogen's phylogenetics may offer valuable insights into the prediction and prevention of potential epidemics; however, lack of uniformity and the number of available USUV sequences worldwide hamper comprehensive investigation.

Aim

The study aimed to investigate USUV spatio-temporal dispersal inter- and intracontinentally and to estimate the dynamics of viral spread within Europe.

Methods

Phylogeographic and phylodynamic analyses were done using advanced phylogenetic methods implemented in Beast 1.10.4 and Beast 2.6.4 software packages.

Results

Herein, we report on a new USUV isolate from Culex pipiens collected in 2019 from Serbia. The results of this research revealed two newly described intercontinental migration events of USUV from Africa to Germany in the 1970s and from Africa to the Middle East (Israel) in the late 90s. Finally, phylodynamic analysis substantiated the ongoing active expansion of USUV in Europe.

Conclusion

The data would imply a high potential for further USUV expansion in Europe. Detailed phylogenetic characterization of the pathogen may offer valuable insights into prediction and prevention of potential epidemics; however, lack of uniformity and number of available USUV sequences worldwide hampers comprehensive investigation. This study draws attention to the need for upscaling USUV surveillance.

Genome-scale metabolic model-based engineering of Escherichia coli enhances recombinant single-chain antibody fragment production

PURPOSE

Escherichia coli is an attractive and cost-effective cell factory for producing recombinant proteins such as single-chain variable fragments (scFvs). AntiEpEX-scFv is a small antibody fragment that has received considerable attention for its ability to target the epithelial cell adhesion molecule (EpCAM), a cancer-associated biomarker of solid tumors. Due to its metabolic burden, scFv recombinant expression causes a remarkable decrease in the maximum specific growth rate of the scFv-producing strain. In the present study, a genome-scale metabolic model (GEM)-guided engineering strategy is proposed to identify gene targets for improved antiEpEX-scFv production in E. coli.

METHODS

In this study, a genome-scale metabolic model of E. coli (iJO1366) and a metabolic modeling tool (FVSEOF) were employed to find appropriate genes to be amplified in order to improve the strain for incresed production of antiEpEX-scFv. To validate the model predictions, one target gene was overexpressed in the parent strain Escherichia coli BW25113 (DE3).

RESULTS

For improving scFv production, we applied the FVSEOF method to identify a number of potential genetic engineering targets. These targets were found to be localized in the glucose uptake system and pentose phosphate pathway. From the predicted targets, the glk gene encoding glucokinase was chosen to be overexpressed in the parent strain Escherichia coli BW25113 (DE3). By overexpressing glk, the growth capacity of the recombinant E. coli strain was recovered. Moreover, the engineered strain with glk overexpression successfully led to increased scFv production.

CONCLUSION

The genome-scale metabolic modeling can be considered for the improvement of the production of other recombinant proteins.

Identification of novel susceptibility genes for non-syndromic cleft lip with or without cleft palate using NGS-based multigene panel testing

For non-syndromic cleft lip with or without cleft palate (ns-CL/P), the proportion of heritability explained by the known risk loci is estimated to be about 30% and is captured mainly by common variants identified in genome-wide association studies. To contribute to the explanation of the "missing heritability" problem for orofacial clefts, a candidate gene approach was taken to investigate the potential role of rare and private variants in the ns-CL/P risk. Using the next-generation sequencing technology, the coding sequence of a set of 423 candidate genes was analysed in 135 patients from the Polish population. After stringent multistage filtering, 37 rare coding and splicing variants of 28 genes were identified. 35% of these genetic alternations that may play a role of genetic modifiers influencing an individual's risk were detected in genes not previously associated with the ns-CL/P susceptibility, including COL11A1, COL17A1, DLX1, EFTUD2, FGF4, FGF8, FLNB, JAG1, NOTCH2, SHH, WNT5A and WNT9A. Significant enrichment of rare alleles in ns-CL/P patients compared with controls was also demonstrated for ARHGAP29, CHD7, COL17A1, FGF12, GAD1 and SATB2. In addition, analysis of panoramic radiographs of patients with identified predisposing variants may support the hypothesis of a common genetic link between orofacial clefts and dental abnormalities. In conclusion, our study has confirmed that rare coding variants might contribute to the genetic architecture of ns-CL/P. Since only single predisposing variants were identified in novel cleft susceptibility genes, future research will be required to confirm and fully understand their role in the aetiology of ns-CL/P.

Rapid Antibiotic Resistance Serial Prediction in Staphylococcus aureus Based on Large-Scale MALDI-TOF Data by Applying XGBoost in Multi-Label Learning

Multidrug resistance has become a phenotype that commonly exists among Staphylococcus aureus and is a serious concern for infection treatment. Nowadays, to detect the antibiotic susceptibility, antibiotic testing is generated based on the level of genomic for cure decision consuming huge of time and labor, while matrix-assisted laser desorption-ionization (MALDI) time-of-flight mass spectrometry (TOF/MS) shows its possibility in high-speed and effective detection on the level of proteomic. In this study, on the basis of MALDI-TOF spectra data of discovery cohort with 26,852 samples and replication cohort with 4,963 samples from Taiwan area and their corresponding susceptibilities to oxacillin and clindamycin, a multi-label prediction model against double resistance using Lowest Power set ensemble with XGBoost is constructed for rapid susceptibility prediction. With the output of serial susceptibility prediction, the model performance can realize 77% of accuracy for the serial prediction, the area under the receiver characteristic curve of 0.93 for oxacillin susceptibility prediction, and the area under the receiver characteristic curve of 0.89 for clindamycin susceptibility prediction. The generated multi-label prediction model provides serial antibiotic resistance, such as the susceptibilities of oxacillin and clindamycin in this study, for S. aureus-infected patients based on MALDI-TOF, which will provide guidance in antibiotic usage during the treatment taking the advantage of speed and efficiency.

Hydrogenophaga crocea sp. nov. associated with cyanobacterial mat isolated from farmland mud

A catalase and oxidase-positive strain BA0156^T was isolated from a cyanobacterial mat collected from the farmland mud cultivated with sugarcane from Ahmednagar, India. The 16S rRNA gene of strain BA0156^T showed the highest percent sequence similarity with Hydrogenophaga borbori LMG 30805^T (98.5%), followed by H. flava DSM 619^T (98.3%) and H. intermedia DSM 5680^T (98.2%). The strain BA0156^T contained the major fatty acids, C_16:0 (25.1%) and C_17:0 cyclo (3.9%), whereas phosphatidylethanolamine and diphosphatidylglycerol were the major polar lipids. The OrthoANI and dDDH values between strain BA0156^T and its closest relative H. borbori LMG 30805^T were 84.6% and 28.3%, respectively. The DNA G+C content of strain BA0156^T was 69.4 mol %. Furthermore, the biochemical and physiological features of strain BA0156^T showed a distinct pattern from their closest phylogenetic neighbours. The phenotypic, genotypic and chemotaxonomic characteristics indicated that the strain BA0156^T represents a new species for which the name Hydrogenophaga crocea (type strain BA0156^T = MCC 3062^T = KCTC 72452^T = JCM 34507^T) is proposed.

Individual variation in susceptibility or exposure to SARS-CoV-2 lowers the herd immunity threshold

Individual variation in susceptibility and exposure is subject to selection by natural infection, accelerating the acquisition of immunity, and reducing herd immunity thresholds and epidemic final sizes. This is a manifestation of a wider population phenomenon known as "frailty variation". Despite theoretical understanding, public health policies continue to be guided by mathematical models that leave out considerable variation and as a result inflate projected disease burdens and overestimate the impact of interventions. Here we focus on trajectories of the coronavirus disease (COVID-19) pandemic in England and Scotland until November 2021. We fit models to series of daily deaths and infer relevant epidemiological parameters, including coefficients of variation and effects of non-pharmaceutical interventions which we find in agreement with independent empirical estimates based on contact surveys. Our estimates are robust to whether the analysed data series encompass one or two pandemic waves and enable projections compatible with subsequent dynamics. We conclude that vaccination programmes may have contributed modestly to the acquisition of herd immunity in populations with high levels of pre-existing naturally acquired immunity, while being critical to protect vulnerable individuals from severe outcomes as the virus becomes endemic.

Molecular Characterization and Clinical Relevance of ANXA1 in Gliomas via 1,018 Chinese Cohort Patients

Annexin A1 (ANXA1) is a calcium-dependent phospholipid-binding protein and has been implicated in multiple functions essential in cancer, including cell proliferation, apoptosis, chemosensitivity, metastasis, and invasion. However, the biological role and clinical behavior of ANXA1 in glioma remain unclear. In this study, RNA-seq (n = 1018 cases) and whole-exome sequencing (WES) (n = 286 cases) data on a Chinese cohort, RNA-seq data with different histological regions of glioblastoma blocks (n = 270 cases), and scRNA-seq data (n = 7630 cells) were used. We used the R software to perform statistical calculations and graph rendering. We found that ANXA1 is closely related to the malignant progression in gliomas. Meanwhile, ANXA1 is significantly associated with clinical behavior. Furthermore, the mutational profile revealed that glioma subtypes classified by ANXA1 expression showed distinct genetic features. Functional analyses suggest that ANXA1 correlates with the immune-related function and cancer hallmark. At a single-cell level, we found that ANXA1 is highly expressed in M2 macrophages and tumor cells of the mesenchymal subtype. Importantly, our result suggested that ANXA1 expression is significant with the patient’s survival outcome. Our study revealed that ANXA1 was closely related to immune response. ANXA1 plays a key factor in M2 macrophages and MES tumor cells. Patients with lower ANXA1 expression levels tended to experience improved survival. ANXA1 may become a valuable factor for the diagnosis and treatment of gliomas in clinical practice.

Simple decision rules to predict local surges in COVID-19 hospitalizations during the winter and spring of 2022

Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes leave many U.S. communities at risk for surges of COVID-19 during the winter and spring of 2022 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations during this period are expected to differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop simple decision rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. These decision rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We showed that these decision rules present reasonable accuracy, sensitivity, and specificity (all ≥80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19 during the winter and spring of 2022. Our proposed decision rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations.

SIGNIFICANCE STATEMENT

In many U.S. communities, the risk of exceeding local healthcare capacity during the winter and spring of 2022 remains substantial since COVID-19 hospitalizations may rise due to seasonal changes, low vaccination coverage, and the emergence of new variants of SARS-CoV-2, such as the omicron variant. Here, we provide simple and easy-to-communicate decision rules to predict whether local hospital occupancy is expected to exceed capacity within a 4- or 8-week period if no additional mitigating measures are implemented. These decision rules can serve as an alert system for local policymakers to respond proactively to mitigate future surges in the COVID-19 hospitalization and minimize risk of overwhelming local healthcare capacity.

Simulation Testbed for Evaluating Distributed Querying and Searching of Mass Spectrometry Big Data in a Network-based Infrastructure

Advance access and reuse mechanisms for large-scale Mass Spectrometry (MS) data are essential for democratizing data for the omics research community and making it adhere to FAIR (Findable, Accessible, Interoperable, Reusable) principles. Although a number of centralized data repositories have been established, they have been limited to search mechanisms that depend on the meta-data associated with these MS datasets. Furthermore, they require constant influx of resources for maintenance. In this paper, we proposed an alternative novel distributed infrastructure for direct MS/MS spectral search. We designed and developed a simulation testbed using concepts from computer networks, queuing theory, and stochastic simulation methods. Results show that a distributed MS search based on raw MS/MS spectra can scale gracefully for up-to 2000 participating nodes, while simultaneously processing queries using the proposed networked infrastructure on the order of milliseconds to a few seconds for up-to a total of fifty billion MS/MS spectra.

SARS-CoV-2 infection risk during delivery of childhood vaccination campaigns: a modelling study

BACKGROUND

The COVID-19 pandemic has disrupted delivery of immunisation services globally. Many countries have postponed vaccination campaigns out of concern about infection risks to staff delivering vaccination, the children being vaccinated and their families. The World Health Organization recommends considering both the benefit of preventive campaigns and the risk of SARS-CoV-2 transmission when making decisions about campaigns during COVID-19 outbreaks, but there has been little quantification of the risks.

METHODS

We modelled excess SARS-CoV-2 infection risk to vaccinators, vaccinees and their caregivers resulting from vaccination campaigns delivered during a COVID-19 epidemic. Our model used population age-structure and contact patterns from three exemplar countries (Burkina Faso, Ethiopia, and Brazil). It combined an existing compartmental transmission model of an underlying COVID-19 epidemic with a Reed-Frost model of SARS-CoV-2 infection risk to vaccinators and vaccinees. We explored how excess risk depends on key parameters governing SARS-CoV-2 transmissibility, and aspects of campaign delivery such as campaign duration, number of vaccinations, and effectiveness of personal protective equipment (PPE) and symptomatic screening.

RESULTS

Infection risks differ considerably depending on the circumstances in which vaccination campaigns are conducted. A campaign conducted at the peak of a SARS-CoV-2 epidemic with high prevalence and without special infection mitigation measures could increase absolute infection risk by 32% to 45% for vaccinators, and 0.3% to 0.5% for vaccinees and caregivers. However, these risks could be reduced to 3.6% to 5.3% and 0.1% to 0.2% respectively by use of PPE that reduces transmission by 90% (as might be achieved with N95 respirators or high-quality surgical masks) and symptomatic screening.

CONCLUSIONS

SARS-CoV-2 infection risks to vaccinators, vaccinees and caregivers during vaccination campaigns can be greatly reduced by adequate PPE, symptomatic screening, and appropriate campaign timing. Our results support the use of adequate risk mitigation measures for vaccination campaigns held during SARS-CoV-2 epidemics, rather than cancelling them entirely.

Covasim: An agent-based model of COVID-19 dynamics and interventions

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.

Paediatric primary care in Germany during the early COVID-19 pandemic: the calm before the storm

OBJECTIVES

Globally, the COVID-19 pandemic has a major impact on healthcare provision. The effects in primary care are understudied. This study aimed to explore changes in consultation numbers and patient management during the COVID-19 pandemic, and to identify challenges for patient care.

DESIGN

Survey of paediatric primary care practices on consultation numbers and patient management changes, and semistructured interviews to identify challenges for patient care. Surveys and interviews were partially linked in an explanatory sequential design to identify patient groups perceived to be at higher risk for worse care during the pandemic.

SETTING

In and around Düsseldorf, a densely populated area in Western Germany. The primary care facilities are spread over an area with approximately 2 million inhabitants.

PARTICIPANTS

Primary care in Germany is provided through practices run by self-employed specialist physicians that are contracted to offer services to patients under public health insurance which is compulsory to the majority of the population. The sample contained 44 paediatric primary care practices in the area, the response rate was 50%.

RESULTS

Numbers of consultations for scheduled developmental examinations remained unchanged compared with the previous year while emergency visits were strongly reduced (mean 87.3 less/week in March-May 2020 compared with 2019, median reduction 55.0%). Children dependent on developmental therapy and with chronic health conditions were identified as patient groups receiving deteriorated care. High patient numbers, including of mildly symptomatic children presenting for health certificates, in combination with increased organisational demands and expected staff outages are priority concerns for the winter.

CONCLUSIONS

Primary care paediatricians offered stable service through the early pandemic but expected strained resources for the upcoming winter. Unambiguous guidance on which children should present to primary care and who should be tested would help to allocate resources appropriately, and this guidance needs to consider age group specific issues including high prevalence of respiratory symptoms, dependency on carers and high contact rates.

Controlling COVID-19 via test-trace-quarantine

Initial COVID-19 containment in the United States focused on limiting mobility, including school and workplace closures. However, these interventions have had enormous societal and economic costs. Here, we demonstrate the feasibility of an alternative control strategy, test-trace-quarantine: routine testing of primarily symptomatic individuals, tracing and testing their known contacts, and placing their contacts in quarantine. We perform this analysis using Covasim, an open-source agent-based model, which has been calibrated to detailed demographic, mobility, and epidemiological data for the Seattle region from January through June 2020. With current levels of mask use and schools remaining closed, we find that high but achievable levels of testing and tracing are sufficient to maintain epidemic control even under a return to full workplace and community mobility and with low vaccine coverage. The easing of mobility restrictions in June 2020 and subsequent scale-up of testing and tracing programs through September provided real-world validation of our predictions. Although we show that test-trace-quarantine can control the epidemic in both theory and practice, its success is contingent on high testing and tracing rates, high quarantine compliance, relatively short testing and tracing delays, and moderate to high mask use. Thus, in order for test-trace-quarantine to control transmission with a return to high mobility, strong performance in all aspects of the program is required.

Detecting in-school transmission of SARS-CoV-2 from case ratios and documented clusters

Claims that in-person schooling has not amplified SARS-CoV-2 transmission are based on similar infection rates in schools and their surrounding communities and limited numbers of documented in-school transmission events. Simulations assuming high in-school transmission suggest that these metrics cannot exclude the possibility that transmission in schools exacerbated overall pandemic risks.

Weighted Gene Co-expression Network Analysis Reveals Different Immunity but Shared Renal Pathology Between IgA Nephropathy and Lupus Nephritis

Both IgA nephropathy (IgAN) and lupus nephritis (LN) are immunity-related diseases with a complex, polygenic, and pleiotropic genetic architecture. However, the mechanism by which the genetic variants impart immunity or renal dysfunction remains to be clarified. In this study, using gene expression datasets as a quantitative readout of peripheral blood mononuclear cell (PBMC)- and kidney-based molecular phenotypes, we analyzed the similarities and differences in the patterns of gene expression perturbations associated with the systematic and kidney immunity in IgAN and LN. Original gene expression datasets for PBMC, glomerulus, and tubule from IgAN and systemic lupus erythematosus (SLE) patients as well as corresponding controls were obtained from the Gene Expression Omnibus (GEO) database. The similarities and differences in the expression patterns were detected according to gene differential expression. Weighted gene co-expression network analysis (WGCNA) was used to cluster and screen the co-expressed gene modules. The disease correlations were then identified by cell-specific and functional enrichment analyses. By combining these results with the genotype data, we identified the differentially expressed genes causatively associated with the disease. There was a significant positive correlation with the kidney expression profile, but no significant correlation with PBMC. Three co-expression gene modules were screened by WGCNA and enrichment analysis. Among them, blue module was enriched for glomerulus and podocyte (P < 0.05) and positively correlated with both diseases (P < 0.05), mainly via immune regulatory pathways. Pink module and purple module were enriched for tubular epithelium and correlated with both diseases (P < 0.05) through predominant cell death and extracellular vesicle pathways, respectively. In genome-wide association study (GWAS) enrichment analysis, blue module was identified as the high-risk gene module that distinguishes LN from SLE and contains PSMB8 and PSMB9, the susceptibility genes for IgAN. In conclusion, IgAN and LN showed different systematic immunity but similarly abnormal immunity in kidney. Immunological pathways may be involved in the glomerulopathy and cell death together with the extracellular vesicle pathway, which may be involved in the tubular injury in both diseases. Blue module may cover the causal susceptibility gene for IgAN and LN.

A participatory modelling approach for investigating the spread of COVID-19 in countries of the Eastern Mediterranean Region to support public health decision-making

Early on in the COVID-19 pandemic, the WHO Eastern Mediterranean Regional Office recognised the importance of epidemiological modelling to forecast the progression of the COVID-19 pandemic to support decisions guiding the implementation of response measures. We established a modelling support team to facilitate the application of epidemiological modelling analyses in the Eastern Mediterranean Region (EMR) countries. Here, we present an innovative, stepwise approach to participatory modelling of the COVID-19 pandemic that engaged decision-makers and public health professionals from countries throughout all stages of the modelling process. Our approach consisted of first identifying the relevant policy questions, collecting country-specific data and interpreting model findings from a decision-maker's perspective, as well as communicating model uncertainty. We used a simple modelling methodology that was adaptable to the shortage of epidemiological data, and the limited modelling capacity, in our region. We discuss the benefits of using models to produce rapid decision-making guidance for COVID-19 control in the WHO EMR, as well as challenges that we have experienced regarding conveying uncertainty associated with model results, synthesising and comparing results across multiple modelling approaches, and modelling fragile and conflict-affected states.

Reduced Gut Microbiome Diversity and Metabolome Differences in Rhinoceros Species at Risk for Iron Overload Disorder

Iron overload disorder (IOD) affects many wildlife species cared for ex situ. Two of the four rhinoceros species in human care, Sumatran rhinoceros (Dicerorhinus sumatrensis) and black rhinoceros (Diceros bicornis), are susceptible, whereas the other two, white rhinoceros (Ceratotherium simum) and greater one-horned (GOH) rhinoceros (Rhinoceros unicornis), are relatively resistant to IOD. Complex interrelationships exist between mammalian hosts, their indigenous gut microbiota, metabolome, physical condition, and iron availability. The goal of this study was to gain insight into these relationships within the family Rhinocerotidae. Specific objectives were to (1) characterize the gut microbiome and metabolome of four rhinoceros species; (2) compare the microbiome and metabolome of IOD-susceptible and IOD-resistant rhinoceros species; and (3) identify variation in the microbiome and metabolome associated with compromised health or disease in IOD-susceptible rhinoceroses. Fecal samples were collected from 31 rhinoceroses (Sumatran rhinoceros, n = 3; black rhinoceros, n = 6; GOH rhinoceros, n = 9; white rhinoceros, n = 13) located at five facilities, and matched fecal aliquots were processed for microbiome and metabolome analyses using 16S rRNA gene sequencing and nuclear magnetic resonance spectroscopy, respectively. Despite the phylogenetic disparity and dissimilar zoo diets of the hosts, the structure of the fecal microbiota of the two IOD-susceptible rhinoceros species were more closely related to each other than to those of the two IOD-resistant species (Bray–Curtis dissimilarity; IOD-susceptible vs. IOD-resistant p-value < 0.001). In addition, IOD-susceptible rhinoceroses exhibited less microbial diversity than their IOD-resistant relatives (Shannon diversity; p-value < 0.001) which could have health implications. Of note, the black rhinoceros was distinct among the four rhinoceros species with the most divergent fecal metabolome; interestingly, it contained higher concentrations of short chain fatty acids. Neither age nor sex were associated with differences in microbial community composition (p = 0.253 and 0.488, respectively) or fecal metabolomic profile (p = 0.634 and 0.332, respectively). Differences in the distal gut microbiomes between IOD-resistant and IOD-susceptible rhinoceroses support hypotheses that gut microbes play a role in host iron acquisition, and further studies and experiments to test these hypotheses are warranted.

Gene Expression Profiles Associated with Brain Aging are Altered in Schizophrenia

Existence of aging associated transcriptional differences in the schizophrenia brain was investigated in RNA sequencing data from 610 postmortem Dorso-Lateral Pre-Frontal Cortex (DLPFC) samples in the CommondMind Consortium (CMC) and the psychENCODE cohorts. This analysis discovered that the trajectory of gene expression changes that occur during brain aging differed between schizophrenia cases and unaffected controls. Mainly, the identified gene expression differences between the diagnosis groups shrank in magnitude following 60 years of age. A differential expression analysis restricted to the 40 to 60 year age group identified 556 statistically significant loci that replicated and had highly consistent gene expression fold changes in the two cohorts. An interaction between age and diagnosis in the wider psychENCODE cohort was also detected. Gene set enrichment analysis discovered disruptions in mitochondria, RNA splicing and phosphoprotein gene pathways. The identified differentially expressed genes in the two cohorts were also significantly enriched in genomic regions associated with schizophrenia although no enrichment was observed for differentially expressed genes identified in the 40 to 60 year age group. This work implicates disruptions to the normal brain aging processes in the pathology of schizophrenia and demonstrates the need for age stratification in schizophrenia postmortem brain gene expression studies.

BioSimulator.jl: Stochastic simulation in Julia

BACKGROUND AND OBJECTIVES

Biological systems with intertwined feedback loops pose a challenge to mathematical modeling efforts. Moreover, rare events, such as mutation and extinction, complicate system dynamics. Stochastic simulation algorithms are useful in generating time-evolution trajectories for these systems because they can adequately capture the influence of random fluctuations and quantify rare events. We present a simple and flexible package, BioSimulator.jl, for implementing the Gillespie algorithm, τ-leaping, and related stochastic simulation algorithms. The objective of this work is to provide scientists across domains with fast, user-friendly simulation tools.

METHODS

We used the high-performance programming language Julia because of its emphasis on scientific computing. Our software package implements a suite of stochastic simulation algorithms based on Markov chain theory. We provide the ability to (a) diagram Petri Nets describing interactions, (b) plot average trajectories and attached standard deviations of each participating species over time, and (c) generate frequency distributions of each species at a specified time.

RESULTS

BioSimulator.jl's interface allows users to build models programmatically within Julia. A model is then passed to the simulate routine to generate simulation data. The built-in tools allow one to visualize results and compute summary statistics. Our examples highlight the broad applicability of our software to systems of varying complexity from ecology, systems biology, chemistry, and genetics.

CONCLUSION

The user-friendly nature of BioSimulator.jl encourages the use of stochastic simulation, minimizes tedious programming efforts, and reduces errors during model specification.

Genetic Drivers of Pancreatic Islet Function

The majority of gene loci that have been associated with type 2 diabetes play a role in pancreatic islet function. To evaluate the role of islet gene expression in the etiology of diabetes, we sensitized a genetically diverse mouse population with a Western diet high in fat (45% kcal) and sucrose (34%) and carried out genome-wide association mapping of diabetes-related phenotypes. We quantified mRNA abundance in the islets and identified 18,820 expression QTL. We applied mediation analysis to identify candidate causal driver genes at loci that affect the abundance of numerous transcripts. These include two genes previously associated with monogenic diabetes (PDX1 and HNF4A), as well as three genes with nominal association with diabetes-related traits in humans (FAM83E, IL6ST, and SAT2). We grouped transcripts into gene modules and mapped regulatory loci for modules enriched with transcripts specific for α-cells, and another specific for δ-cells. However, no single module enriched for β-cell-specific transcripts, suggesting heterogeneity of gene expression patterns within the β-cell population. A module enriched in transcripts associated with branched-chain amino acid metabolism was the most strongly correlated with physiological traits that reflect insulin resistance. Although the mice in this study were not overtly diabetic, the analysis of pancreatic islet gene expression under dietary-induced stress enabled us to identify correlated variation in groups of genes that are functionally linked to diabetes-associated physiological traits. Our analysis suggests an expected degree of concordance between diabetes-associated loci in the mouse and those found in human populations, and demonstrates how the mouse can provide evidence to support nominal associations found in human genome-wide association mapping.

Gap Junction Modulation of Low-Frequency Oscillations in the Cerebellar Granule Cell Layer

Local field potential (LFP) oscillations in the granule cell layer (GCL) of the cerebellar cortex have been identified previously in the awake rat and monkey during immobility. These low-frequency oscillations are thought to be generated through local circuit interactions between Golgi cells and granule cells within the GCL. Golgi cells display rhythmic firing and pacemaking properties, and also are electrically coupled through gap junctions within the GCL. Here, we tested if gap junctions in the rat cerebellar cortex contribute to the generation of LFP oscillations in the GCL. We recorded LFP oscillations under urethane anesthesia, and examined the effects of local infusion of gap junction blockers on 5-15 Hz oscillations. Local infusion of the gap junction blockers carbenoxolone and mefloquine resulted in significant decreases in the power of oscillations over a 30-min period, but the power of oscillations was unchanged in control experiments following vehicle injections. In addition, infusion of gap junction blockers had no significant effect on multi-unit activity, suggesting that the attenuation of low-frequency oscillations was likely due to reductions in electrical coupling rather than a decreased excitability within the granule cell layer. Our results indicate that electrical coupling among the Golgi cell networks in the cerebellar cortex contributes to the local circuit mechanisms that promote the occurrence of GCL LFP slow oscillations in the anesthetized rat.

Dissemination of 6S RNA among bacteria

6S RNA is a highly abundant small non-coding RNA widely spread among diverse bacterial groups. By competing with DNA promoters for binding to RNA polymerase (RNAP), the RNA regulates transcription on a global scale. RNAP produces small product RNAs derived from 6S RNA as template, which rearranges the 6S RNA structure leading to dissociation of 6S RNA:RNAP complexes. Although 6S RNA has been experimentally analysed in detail for some species, such as Escherichia coli and Bacillus subtilis, and was computationally predicted in many diverse bacteria, a complete and up-to-date overview of the distribution among all bacteria is missing. In this study we searched with new methods for 6S RNA genes in all currently available bacterial genomes. We ended up with a set of 1,750 6S RNA genes, of which 1,367 are novel and bona fide, distributed among 1,610 bacteria, and had a few tentative candidates among the remaining 510 assembled bacterial genomes accessible. We were able to confirm two tentative candidates by Northern blot analysis. We extended 6S RNA genes of the Flavobacteriia significantly in length compared to the present Rfam entry. We describe multiple homologs of 6S RNAs (including split 6S RNA genes) and performed a detailed synteny analysis.