Long-term prognosis of idiopathic ventricular fibrillation: An eighteen-year experience from a tertiary center

INTRODUCTION AND OBJECTIVES

Idiopathic ventricular fibrillation (IVF) is diagnosed in patients who survive sudden cardiac arrest (SCA), preferably with documented ventricular fibrillation (VF), without any identifiable structural or electrical abnormality. Current evidence provides limited guidance on the diagnosis and follow-up of these patients. Our aim was to assess the clinical outcomes of survivors of an aborted SCA attributed to IVF.

METHODS

We retrospectively collected clinical data from all patients who survived SCA and implanted a cardiac defibrillator (ICD) between 2005 and 2023.

RESULTS

A total of 38 patients, 36.8% female, with a mean age of 44±14 years old were included. Median follow-up time was 8.7 years (interquartile range (IQR) 4.7-14.7 years). All patients underwent a comprehensive diagnostic evaluation that excluded structural and coronary disease. During follow-up, underlying diagnoses were established in 34.2% of the whole cohort. Genetic testing, performed in 37.2%, revealed underlying diagnoses in 57.1% of those tested, compared to only 26.3% of patients who did not undergo genetic testing [p=0.035, OR=5.1 (95% confidence interval (CI) 1.2-21.5)]. Mortality was 10.5% (due to non-arrhythmic causes) and 36.8% patients received appropriate therapies with a median time to first ICD therapy of 39 [5.4-47.3] months.

CONCLUSION(S)

Etiological diagnosis and recurrence prediction in patients with IVF remains challenging, even with extensive diagnostic evaluation and long-term follow-up. In our study, genetic testing enhanced diagnostic yield. Consistent with previous findings, our cohort experienced a notable arrhythmic recurrence, with no cardiac deaths, underlining the pivotal role of ICD implantation in these patients.

Orogenital and anal infection by Chlamydia trachomatis, Neisseria gonorrhoeae, Mycoplasma genitalium, and other sexually transmitted infections in men who have sex with men in Lisbon

BACKGROUND

Men who have sex with men (MSM) are at risk for sexually transmitted infections (STIs), but more data on extragenital carriage are needed.

AIM

We assessed the genital and extragenital prevalence of bacterial and other STIs in MSM in a Lisbon sexual health clinic.

METHODS

We screened oral, anal, and urine samples of MSM visiting the GAT-CheckpointLX clinic June 2017-December 2021 for Chlamydia trachomatis (including lymphogranuloma venereum, LGV), Neisseria gonorrhoeae, Mycoplasma genitalium, Trichomonas vaginalis, Mycoplasma hominis, Ureaplasma urealyticum, and U. parvum. Ano-oro-genital lesions were tested for LGV, Treponema pallidum, and Herpes Simplex Virus. Blood was tested for HIV and T. pallidum antibodies.

RESULTS

N. gonorrhoeae was found in 16.6% of the MSM followed by C. trachomatis (13.2%), M. genitalium (10.3%) and T. vaginalis (0.2%). The most frequent occurrence was anorectal (C. trachomatis, M. genitalium) and oral (N. gonorrhoeae). We found high carriage of U. urealyticum (36.1%) and M. hominis (22.1%). LGV was detected in 21.8% of chlamydia-positive anorectal swabs. Syphilis was detected in 22.6% of tested MSM, while 13.8% had HIV. Gonorrhoea and chlamydia were significantly more prevalent in MSM with concomitant HIV or syphilis.

CONCLUSION

The substantial extragenital prevalence of bacterial STIs in MSM, and HIV and syphilis coinfections, suggest screening has value in identifying hidden carriage and in contributing for providing better care.

Reconstruction of cell-specific models capturing the influence of metabolism on DNA methylation in cancer

The imbalance of epigenetic regulatory mechanisms such as DNA methylation, which can promote aberrant gene expression profiles without affecting the DNA sequence, may cause the deregulation of signaling, regulatory, and metabolic processes, contributing to a cancerous phenotype. Since some metabolites are substrates and cofactors of epigenetic regulators, their availability can be affected by characteristic cancer cell metabolic shifts, feeding cancer onset and progression through epigenetic deregulation. Hence, there is a need to study the influence of cancer metabolic reprogramming in DNA methylation to design new effective treatments. In this study, a generic Genome-Scale Metabolic Model (GSMM) of a human cell, integrating DNA methylation or demethylation reactions, was obtained and used for the reconstruction of Genome-Scale Metabolic Models enhanced with Enzymatic Constraints using Kinetic and Omics data (GECKOs) of 31 cancer cell lines. Furthermore, cell-line-specific DNA methylation levels were included in the models, as coefficients of a DNA composition pseudo-reaction, to depict the influence of metabolism over global DNA methylation in each of the cancer cell lines. Flux simulations demonstrated the ability of these models to provide simulated fluxes of exchange reactions similar to the equivalent experimentally measured uptake/secretion rates and to make good functional predictions. In addition, simulations found metabolic pathways, reactions and enzymes directly or inversely associated with the gene promoter methylation. Two potential candidates for targeted cancer epigenetic therapy were identified.

BioISO: An Objective-Oriented Application for Assisting the Curation of Genome-Scale Metabolic Models

As the reconstruction of Genome-Scale Metabolic Models (GEMs) becomes standard practice in systems biology, the number of organisms having at least one metabolic model is peaking at an unprecedented scale. The automation of laborious tasks, such as gap-finding and gap-filling, allowed the development of GEMs for poorly described organisms. However, the quality of these models can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. Biological networks constraint-based In Silico Optimisation (BioISO) is a computational tool aimed at accelerating the reconstruction of GEMs. This tool facilitates manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). In this assessment, BioISO is better suited to reducing the search space for errors and gaps in metabolic networks by identifying smaller ratios of dead-end metabolites. Furthermore, BioISO was used as Meneco's gap-finding algorithm to reduce the number of proposed solutions for filling the gaps.

Increasing and sustaining blood-borne virus screening in Spain and Portugal throughout the COVID-19 pandemic: a multi-center quality improvement intervention

Background

Around 57,000 people in Spain and Portugal currently living with HIV or chronic hepatitis C are unaware of their infection. The COVID-19 pandemic severely disrupted screening efforts for these infections. We designed an intervention to increase and sustain opportunistic blood-borne virus (BBV) screening and linkage to care (SLTC) by implementing the TEST model.

Methods

The Plan Do Study Act (PDSA) method of quality improvement (QI) was implemented in 8 healthcare organizations (HCOs), including four hospitals, two clusters of community health centers, and two community-based organizations (CBOs). Baseline assessment included a review of BBV SLTC practices, testing volume, and results 12 months before the intervention. Changes in BBV testing rates over time were measured before, during, and after the COVID-19 lockdowns in 2020. A mixed ANOVA model was used to analyze the possible effect on testing volumes among HCOs over the three study periods.

Intervention

BBV testing was integrated into normal clinical flow in all HCOs using existing clinical infrastructure and staff. Electronic health record (EHR) systems were modified whenever possible to streamline screening processes, implement systemic institutional policy changes, and promote QI.

Results

Two years after the launch of the intervention in screening practices, testing volumes increased by 116%, with formal healthcare settings recording larger increases than CBOs. The start of the COVID-19 lockdowns was accompanied by a global 60% decrease in testing in all HCOs. Screening emergency department patients or using EHR systems to automate screening showed the highest resilience and lowest reduction in testing. HCOs recovered 77% of their testing volume once the lockdowns were lifted, with CBOs making the fullest recovery. Globally, enhanced screening techniques enabled HCOs to diagnose a total of 1,860 individuals over the research period.

Conclusions

Implementation of the TEST model enabled HCOs to increase and sustain BBV screening, even during COVID-19 lockdowns. Although improvement in screening was noted in all HCOs, additional work is needed to develop strong patient linkage to care models in challenging times, such as global pandemics.

Comparison of three fluids for calibration of the new Periotron® 8010

BACKGROUND

The aim of the present study was to calibrate the Periotron® model 8010 with volumes of three different fluids (distilled water, serum, and saliva) and to identify which of the three is the most reliable, feasible, and reproducible for routine calibration.

MATERIAL AND METHODS

A total of 450 samples of Periopaper® were divided into three groups (150 each per group): distilled water, serum matrix and saliva. A calibration curve was run with 0.25, 0.50, 0.75, 1.00 and 1.25 µl of each of the fluids, and the results were determined in Periotron units (PU). Statistical analysis was performed with one-way ANOVA followed by Bonferroni's post hoc test and a linear equation.

RESULTS

Distilled water presented the lowest levels of PU at all volumes, while serum showed the highest levels at high volumes. Linear regression equations rendered similar slopes for saliva and distilled water, while serum was statistically different. Saliva presented a reproduction percentage of 99.7%, which indicated better accuracy and precision than serum and distilled water.

CONCLUSIONS

Saliva is more reliable and accurate than water or serum for the purpose of calibration of the Periotron® model 8010, though it shares drawbacks with serum. Distilled water is more easily available and does not require any additional procedure, in addition to producing a similar slope to saliva and a smaller deviation from the media than serum.

Viral genetic clustering and transmission dynamics of the 2022 mpox outbreak in Portugal

Pathogen genome sequencing during epidemics enhances our ability to identify and understand suspected clusters and investigate their relationships. Here, we combine genomic and epidemiological data of the 2022 mpox outbreak to better understand early viral spread, diversification and transmission dynamics. By sequencing 52% of the confirmed cases in Portugal, we identified the mpox virus sublineages with the highest impact on case numbers and fitted them into a global context, finding evidence that several international sublineages probably emerged or spread early in Portugal. We estimated a 62% infection reporting rate and that 1.3% of the population of men who have sex with men in Portugal were infected. We infer the critical role played by sexual networks and superspreader gatherings, such as sauna attendance, in the dissemination of mpox virus. Overall, our findings highlight genomic epidemiology as a tool for the real-time monitoring and control of mpox epidemics, and can guide future vaccine policy in a highly susceptible population.

The first multi-tissue genome-scale metabolic model of a woody plant highlights suberin biosynthesis pathways in Quercus suber

Over the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behaviour at the tissue and multi-tissue level under different environmental conditions. Quercus suber, also known as the cork oak tree, is one of the most important forest communities of the Mediterranean/Iberian region. In this work, we present the genome-scale metabolic model of the Q. suber (iEC7871). The metabolic model comprises 7871 genes, 6231 reactions, and 6481 metabolites across eight compartments. Transcriptomics data was integrated into the model to obtain tissue-specific models for the leaf, inner bark, and phellogen, with specific biomass compositions. The tissue-specific models were merged into a diel multi-tissue metabolic model to predict interactions among the three tissues at the light and dark phases. The metabolic models were also used to analyse the pathways associated with the synthesis of suberin monomers, namely the acyl-lipids, phenylpropanoids, isoprenoids, and flavonoids production. The models developed in this work provide a systematic overview of the metabolism of Q. suber, including its secondary metabolism pathways and cork formation.

Bilateral Synchronous Olecranon Apophyseal Fracture in a Teenager: A Case Report

Introduction

While elbow fractures are frequently observed in children, isolated apophyseal detachments of the olecranon are rare, accounting for just 1.7% of all elbow fractures in healthy children. These fractures have been found to have a large positive likelihood ratio for osteogenesis imperfecta (OI). Within the population of patients with OI, the incidence is 13.5%, with 6% sustaining bilateral fractures. There are, however, very few reports of synchronous bilateral fractures.

Case Report

A 15-year-old boy sustained a bilateral olecranon apophyse fracture, AO/OTA 21u-M/7 (displaced on the right side and minimal displacement on the left) after a low-energy fall. The patient was submitted on the same day to surgical treatment (open reduction and internal fixation with AO tension band wiring technique) on the right elbow and nonoperative treatment with a cast on the left side. Exome sequencing excluded mutations related to OI.

Conclusion

Apophyseal avulsion fractures of the olecranon may not be pathognomonic of OI, However, orthopedists should exercise caution and remain alert to the potential occurrence in patients who experience displaced, isolated olecranon fractures due to low-energy mechanisms, particularly if they have a history of previous fractures. The clinical genetic evaluation was recommended because of clinical suspicion of OI and because patient management can be significantly influenced by genetic testing, particularly when a genetic syndrome is identified.

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.

A systematic evaluation of deep learning methods for the prediction of drug synergy in cancer

One of the main obstacles to the successful treatment of cancer is the phenomenon of drug resistance. A common strategy to overcome resistance is the use of combination therapies. However, the space of possibilities is huge and efficient search strategies are required. Machine Learning (ML) can be a useful tool for the discovery of novel, clinically relevant anti-cancer drug combinations. In particular, deep learning (DL) has become a popular choice for modeling drug combination effects. Here, we set out to examine the impact of different methodological choices on the performance of multimodal DL-based drug synergy prediction methods, including the use of different input data types, preprocessing steps and model architectures. Focusing on the NCI ALMANAC dataset, we found that feature selection based on prior biological knowledge has a positive impact-limiting gene expression data to cancer or drug response-specific genes improved performance. Drug features appeared to be more predictive of drug response, with a 41% increase in coefficient of determination (R2) and 26% increase in Spearman correlation relative to a baseline model that used only cell line and drug identifiers. Molecular fingerprint-based drug representations performed slightly better than learned representations-ECFP4 fingerprints increased R2 by 5.3% and Spearman correlation by 2.8% w.r.t the best learned representations. In general, fully connected feature-encoding subnetworks outperformed other architectures. DL outperformed other ML methods by more than 35% (R2) and 14% (Spearman). Additionally, an ensemble combining the top DL and ML models improved performance by about 6.5% (R2) and 4% (Spearman). Using a state-of-the-art interpretability method, we showed that DL models can learn to associate drug and cell line features with drug response in a biologically meaningful way. The strategies explored in this study will help to improve the development of computational methods for the rational design of effective drug combinations for cancer therapy.

Mitochondrial redox impairment and enhanced autophagy in peripheral blood mononuclear cells from type 1 diabetic patients

Type 1 diabetes (T1D) involves critical metabolic disturbances that contribute to an increased cardiovascular risk. Leukocytes are key players in the onset of atherosclerosis due to their interaction with the endothelium. However, whether mitochondrial redox impairment, altered bioenergetics and abnormal autophagy in leukocytes contribute to T1D physiopathology is unclear. In this study we aimed to evaluate the bioenergetic and redox state of peripheral blood mononuclear cells (PBMCs) from T1D patients in comparison to those from healthy subjects, and to assess autophagy induction and leukocyte-endothelial interactions. T1D patients presented lower levels of fast-acting and total antioxidants in their blood, and their leukocytes produced higher amounts of total reactive oxygen species (ROS) and superoxide radical with respect to controls. Basal and ATP-linked respiration were similar in PBMCs from T1D and controls, but T1D PBMCs exhibited reduced spare respiratory capacity and a tendency toward decreased maximal respiration and reduced non-mitochondrial respiration, compared to controls. The autophagy markers P-AMPK, Beclin-1 and LC3-II/LC3-I were increased, while P62 and NBR1 were decreased in T1D PBMCs versus those from controls. Leukocytes from T1D patients displayed lower rolling velocity, higher rolling flux and more adhesion to the endothelium versus controls. Our findings show that T1D impairs mitochondrial function and promotes oxidative stress and autophagy in leukocytes, and suggest that these mechanisms contribute to an increased risk of atherosclerosis by augmenting leukocyte-endothelial interactions.

Systems Biology in ELIXIR: modelling in the spotlight

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Aortic valve calcium score: does it correlate with mean transaortic gradient?

Introduction

Aortic valvular calcium score (AVCS) is useful in patients with aortic stenosis for whom echocardiography was not conclusive in grading its severity. Transcatheter aortic valve implantation (TAVI) is an established procedure of treatment in symptomatic severe AS. The burden of aortic valve calcification has been associated with some TAVI related complications (as perivalvular leaks), but at the same time it is well accepted that some degree of calcification is needed to ensure stable anchoring of the prosthesis to the aortic annulus.

Purpose

To assess if there is a correlation between aortic valve calcium score and mean transvalvular gradient 6 months after TAVI – is a higher AVCS correlated with lower mean transaortic gradient after TAVI?

Methods

We performed a single-center, retrospective cohort study including patients who underwent TAVI with a preoperative standardized contrast enhanced MSCT with AVCS available. Clinical and echocardiographic data were collected previously to TAVI (pre-TAVI) and at 6 months follow up (6M-FUP).

Results

A total of 187 patients were included, with 54% female and a mean age of 79.4±9.0 years old. Most patients had tricuspid aortic valve (95.7%); 5 patients had aortic bicuspidy and 3 had aortic valve bioprothesis. Concerning the valve type, 73.3% had new generation prosthesis and the main valve used was the CoreValve Evolut Pro (33.7%). Also, 38,5% needed balloon pre-dilation before TAVI. The mean pre-TAVI aortic transvalvular maximum and mean gradients were 76.5±23.2 mmHg and 48.3±15.5 mmHg, respectively; mean aortic valve area was 0.75±0.16 cm2. The mean AVCS was 2851±1524 AU (Agaston Units); 81.2% of women had AVCS>1300 AU and 74.4% men had AVCS >2000 AU. Comparing transvalvular aortic gradients previously and 6M-FUP after TAVI, there was an average differential of maximum gradient of 61±22 mmHg and of mean gradient of 40±15 mmHg. A negative and weak correlation was found between the AVCS and the maximum gradient (pearson coefficient of −0.181, p=0.02) and between mean gradient at 6M-FUP (pearson coefficient of −0.191, p=0.014).

Discussion and conclusion

AVCS is a significant predictor for death, stroke and perivalvular leaks after TAVI. On the other hand, high AVCS is associated with better seating in the native annulus during deployment. Nevertheless, high AVCS did not strongly correlated with mean transaortic gradient 6 months after TAVI.

Funding Acknowledgement

Type of funding sources: None.

Evaluating molecular representations in machine learning models for drug response prediction and interpretability

Machine learning (ML) is increasingly being used to guide drug discovery processes. When applying ML approaches to chemical datasets, molecular descriptors and fingerprints are typically used to represent compounds as numerical vectors. However, in recent years, end-to-end deep learning (DL) methods that can learn feature representations directly from line notations or molecular graphs have been proposed as alternatives to using precomputed features. This study set out to investigate which compound representation methods are the most suitable for drug sensitivity prediction in cancer cell lines. Twelve different representations were benchmarked on 5 compound screening datasets, using DeepMol, a new chemoinformatics package developed by our research group, to perform these analyses. The results of this study show that the predictive performance of end-to-end DL models is comparable to, and at times surpasses, that of models trained on molecular fingerprints, even when less training data is available. This study also found that combining several compound representation methods into an ensemble can improve performance. Finally, we show that a post hoc feature attribution method can boost the explainability of the DL models.

A pipeline for the reconstruction and evaluation of context-specific human metabolic models at a large-scale

Constraint-based (CB) metabolic models provide a mathematical framework and scaffold for in silico cell metabolism analysis and manipulation. In the past decade, significant efforts have been done to model human metabolism, enabled by the increased availability of multi-omics datasets and curated genome-scale reconstructions, as well as the development of several algorithms for context-specific model (CSM) reconstruction. Although CSM reconstruction has revealed insights on the deregulated metabolism of several pathologies, the process of reconstructing representative models of human tissues still lacks benchmarks and appropriate integrated software frameworks, since many tools required for this process are still disperse across various software platforms, some of which are proprietary. In this work, we address this challenge by assembling a scalable CSM reconstruction pipeline capable of integrating transcriptomics data in CB models. We combined omics preprocessing methods inspired by previous efforts with in-house implementations of existing CSM algorithms and new model refinement and validation routines, all implemented in the Troppo Python-based open-source framework. The pipeline was validated with multi-omics datasets from the Cancer Cell Line Encyclopedia (CCLE), also including reference fluxomics measurements for the MCF7 cell line. We reconstructed over 6000 models based on the Human-GEM template model for 733 cell lines featured in the CCLE, using MCF7 models as reference to find the best parameter combinations. These reference models outperform earlier studies using the same template by comparing gene essentiality and fluxomics experiments. We also analysed the heterogeneity of breast cancer cell lines, identifying key changes in metabolism related to cancer aggressiveness. Despite the many challenges in CB modelling, we demonstrate using our pipeline that combining transcriptomics data in metabolic models can be used to investigate key metabolic shifts. Significant limitations were found on these models ability for reliable quantitative flux prediction, thus motivating further work in genome-wide phenotype prediction.

Genome-scale models of human metabolism are promising tools capable of contextualising large omics datasets within a framework that enables analysis and manipulation of metabolic phenotypes. Despite various successes in applying these methods to provide mechanistic hypotheses for deregulated metabolism in disease, there is no standardized workflow to extract these models using existing methods and the tools required to do so are mostly implemented using proprietary software. We have assembled a generic pipeline to extract and validate context-specific metabolic models using multi-omics datasets and implemented it using the troppo framework. We first validate our pipeline using MCF7 cell line models and assess their ability to predict lethal gene knockouts as well as flux activity using multi-omics data. We also demonstrate how this approach can be generalized for large-scale transcriptomics datasets and used to generate insights on the metabolic heterogeneity of cancer and relevant features for other data mining approaches. The pipeline is available as part of an open-source framework that is generic for a variety of applications.

merlin, an improved framework for the reconstruction of high-quality genome-scale metabolic models

Genome-scale metabolic models have been recognised as useful tools for better understanding living organisms' metabolism. merlin (https://www.merlin-sysbio.org/) is an open-source and user-friendly resource that hastens the models' reconstruction process, conjugating manual and automatic procedures, while leveraging the user's expertise with a curation-oriented graphical interface. An updated and redesigned version of merlin is herein presented. Since 2015, several features have been implemented in merlin, along with deep changes in the software architecture, operational flow, and graphical interface. The current version (4.0) includes the implementation of novel algorithms and third-party tools for genome functional annotation, draft assembly, model refinement, and curation. Such updates increased the user base, resulting in multiple published works, including genome metabolic (re-)annotations and model reconstructions of multiple (lower and higher) eukaryotes and prokaryotes. merlin version 4.0 is the only tool able to perform template based and de novo draft reconstructions, while achieving competitive performance compared to state-of-the art tools both for well and less-studied organisms.

UPIMAPI, reCOGnizer and KEGGCharter: bioinformatics tools for functional annotation and visualization of (meta)-omics datasets

Omics and meta-omics technologies are powerful approaches to explore microorganisms’ functions, but the sheer size and complexity of omics datasets often turn the analysis into a challenging task. Software developed for omics and meta-omics analyses, together with knowledgebases encompassing information on genes, proteins, taxonomic and functional annotation, among other types of information, are valuable resources for analyzing omics data. Although several bioinformatics resources are available for meta-omics analyses, many require significant computational expertise. Web interfaces are more user-friendly, but often struggle to handle large data files, such as those obtained in metagenomics, metatranscriptomics, or metaproteomics experiments.

In this work, we present three novel bioinformatics tools, which are available through user-friendly command-line interfaces, can be run sequentially or stand-alone, and combine popular resources for functional annotation. UPIMAPI performs sequence homology-based annotation and obtains data from UniProtKB (e.g., protein names, EC numbers, Gene Ontology, Taxonomy, cross-references to external databases). reCOGnizer performs multithreaded domain homology-based annotation of protein sequences with several functional databases (i.e., CDD, NCBIfam, Pfam, Protein Clusters, SMART, TIGRFAM, COG and KOG) and in addition, obtains information on domain names and descriptions and EC numbers. KEGGCharter represents omics results, including differential gene expression, in KEGG metabolic pathways. In addition, it shows the taxonomic assignment of the enzymes represented, which is particularly useful in metagenomics studies in which several microorganisms are present.

reCOGnizer, UPIMAPI and KEGGCharter together provide a comprehensive and complete functional characterization of large datasets, facilitating the interpretation of microbial activities in nature and in biotechnological processes.

Transitions Between Preexposure Prophylaxis Eligibility States and HIV Infection in the Lisbon Cohort of HIV-Negative Men Who Have Sex With Men: A Multistate Model Analysis

We aimed to describe transitions between preexposure prophylaxis (PrEP) eligibility and human immunodeficiency virus (HIV) infection among HIV-negative men who have sex with men (MSM). We used data from 1,885 MSM, who had not used PrEP, enrolled in the Lisbon Cohort of MSM, with at least 2 consecutive measurements of PrEP eligibility from 2014-2020. A time-homogeneous Markov multistate model was applied to describe the transitions between states of PrEP eligibility-eligible and ineligible-and from these to HIV infection (HIV). The intensities of the transitions were closer for ineligible-to-eligible and eligible-to-ineligible transitions (intensity ratio, 1.107, 95% confidence interval (CI): 1.080, 1.176), while the intensity of the eligible-to-HIV transition was higher than that for ineligible-to-HIV transition (intensity ratio, 9.558, 95% CI: 0.738, 65.048). The probabilities of transitions increased with time; for 90 days, the probabilities were similar for the ineligible-to-eligible and eligible-to-ineligible transitions (0.285 (95% CI: 0.252, 0.319) vs. 0.258 (95% CI: 0.228, 0.287)), while the eligible-to-HIV transition was more likely than ineligible-to-HIV (0.004 (95% CI: 0.003, 0.007) vs. 0.001 (95% CI: 0.001, 0.008)) but tended to become closer with time. Being classified as ineligible was a short-term indicator of a lower probability of acquiring HIV. Once an individual moved to eligible, he was at a higher risk of seroconversion, demanding a timely delivery ofPrEP.

The effect of different pulp capping methods on the intrapulpal temperature when using light-cured procedures

Background

To evaluate the intra-pulpal temperature during different direct pulp capping methods and light-cured procedures.

Material and Methods

Class I preparations 5 mm long, 4 mm wide and 4 mm deep were performed in extracted third molars, leaving 0.5 mm of dentin at the pulpal floor with a 1 mm diameter of pulp exposure. Teeth were placed in a customized oral cavity chamber simulator in which the initial temperature was standardized at 36oC. The overall temperature variations (oC) in the pulp chamber during the light-activation processes were recorded live using an infrared camera (FLIR ONE PRO, FLIR Systems). The liners and bases evaluated were: Dycal (Dentisply), TheraCal LC (Bisco), Biodentin (Septodent), Vitrebond Plus (3M/ESPE), and Fuji IX GP (GC), followed by restoration with a bulk fill composite (EvoCeram Bulk Fill, Ivoclar Vivadent). All light-activation procedures were performed with the VALO Grand (Ultradent) light-curing unit. A power analysis was conducted to determine the sample size to provide a power of at least 0.8 with α=0.05. Statistical analyses were performed using ANOVA and Tukey’s test for multiple comparisons.

Results

The intrapulpal temperature increased above a 10oC to 20oC threshold difference for all liners and bases that were light cured. When added as second layers, neither of those could provide thermal insulation following additional light-activated procedures (p=0.25). The higher the number of procedures requiring light-activation, the longer the pulp temperature remained in those increased temperature thresholds.

Conclusions

For direct pulp capping procedures, a reduced number light activation procedures should be indicated to reduce the time intra-pulpal temperature rises above a 10oC threshold.

Key words:Liner, base, calcium hydroxide, glass ionomer, dental adhesive, bulkfill composite.

Color difference between the vita classical shade guide and composite veneers using the dual-layer technique

Background

The purpose of this in vitro study was to evaluate the color difference between the Vita Classical Shade Guide and composite veneers using the dual-layer technique.

Material and Methods

Thirty samples were fabricated using a custom-made mold (Easy Layering Shade Guide Kit, 3M) using two resin composites: Filtek Supreme Ultra (3M); and Estelite Omega (Tokuyama) (n=3). The composite veneers were made by layering the different enamel and body or dentin shades from each composite. The color measurements were taken using a spectrophotometer (Vita Easyshade V®, Vita Zahnfabrik). The ΔE00 between the Vita Classical Shade Guide (Vita Zahnfabrik) and the composite veneers were calculated using the CIEDE2000 formula.

Results

For the composite veneers using Filtek Supreme Ultra, the best match for A1 Vita shade was achieved layering either EA1 with DA2 or DA3; EA2 with DA1 or DA2 (ΔE00= 1.53 ~ 1.96 ± 0.4). For A2 Vita shade the best match would be EA3 with DA3 or EA3 with DA2 (ΔE00= 1.40 ~ 1.85 ± 0.1); or for A3 Vita shade the best match would be EA3 with DA2 2.50±(0.6). For the composite veneers using Estelite Omega, the were no best match for neither A1, A2 or A3 Vita shade (ΔE00> 2.5).

Conclusions

The combination of enamel and dentin shades from Filtek Supreme Ultra provided acceptable color match for A1, A2 and A3 shades from the Vita Shade Guide, while Estelite Omega did not provide acceptable color match for any of the Vita Shade Guide standard shades tested.

Key words:Color, color matching, optical properties, resin composite, layering.

Effect of dental headlights spectrum on the polymerization and working time of light-cured resin composites

Background

The use of dental headlights is a common practice to better illuminate the operatory field and achieve excellence in restorative dentistry. However, visible light-cured dental materials can have reduced working time under headlight illumination. The aim of this study was to evaluate the influence of the spectral irradiance power of two dental headlights on the degree of polymerization and working time of light-curable dental composites.

Material and Methods

Two headlights, StarLight Nano 3 (StarMed) (SN) and Zeon Endevour XL (Orascoptic) (ZE) were characterized using a spectrophotometer coupled to an integrating sphere (MARC® Light Collector, BlueLight Analytics). The degree of conversion of the two composites, Filtek Supreme (3M) and Tetric Prime (Ivoclar Vivadent), was evaluated using an FTIR spectroscope (NicoletTM iS20, Thermo Fisher).

Results

Both headlights emitted a significant amount of blue light. The Zeon headlamp without filter emitted a broader spectrum with lower blue intensity and higher CRI than the White LED of the Nano 3. The Zeon headlamp with the blue blocking filter emitted a broader spectrum than the Orange LED of the Nano 3. There were no differences in the degree of conversion and working time of the Filtek Supreme and Tetric Prime composites when illuminated by the different headlamps. Both Zeon and the White LED of the Nano 3 were capable to cure the composites within only 5-10 minutes of irradiation. There were no changes in the degree of conversion of the composites when the Orange LED of the Nano 3 or the blue blocking filter of the Zeon were used.

Conclusions

Both headlights reduced the working time of light-cured materials. The use of orange filters prevented the composite polymerization and maintained the working time.

Key words:Surgical Headlight, degree of conversion, working time, light-curing.

Reconstruction of tissue-specific genome-scale metabolic models for human cancer stem cells

Cancer Stem Cells (CSCs) contribute to cancer aggressiveness, metastasis, chemo/radio-therapy resistance, and tumor recurrence. Recent studies emphasized the importance of metabolic reprogramming of CSCs for the maintenance and progression of the cancer phenotype through both the fulfillment of the energetic requirements and the supply of substrates fundamental for fast-cell growth, as well as through metabolite-induced epigenetic regulation. Therefore, it is of paramount importance to develop therapeutic strategies tailored to target the metabolism of CSCs. In this work, we built computational Genome-Scale Metabolic Models (GSMMs) for CSCs of different tissues. Flux simulations were then used to predict metabolic phenotypes, identify potential therapeutic targets, and spot already-known Transcription Factors (TFs), miRNAs and antimetabolites that could be used as part of drug repurposing strategies against cancer. Results were in accordance with experimental evidence, provided insights of new metabolic mechanisms for already known agents, and allowed for the identification of potential new targets and compounds that could be interesting for further in vitro and in vivo validation.

Generative Deep Learning for Targeted Compound Design

In the past few years, de novo molecular design has increasingly been using generative models from the emergent field of Deep Learning, proposing novel compounds that are likely to possess desired properties or activities. De novo molecular design finds applications in different fields ranging from drug discovery and materials sciences to biotechnology. A panoply of deep generative models, including architectures as Recurrent Neural Networks, Autoencoders, and Generative Adversarial Networks, can be trained on existing data sets and provide for the generation of novel compounds. Typically, the new compounds follow the same underlying statistical distributions of properties exhibited on the training data set Additionally, different optimization strategies, including transfer learning, Bayesian optimization, reinforcement learning, and conditional generation, can direct the generation process toward desired aims, regarding their biological activities, synthesis processes or chemical features. Given the recent emergence of these technologies and their relevance, this work presents a systematic and critical review on deep generative models and related optimization methods for targeted compound design, and their applications.

ViralFP: A Web Application of Viral Fusion Proteins

Viral fusion proteins are attached to the membrane of enveloped viruses (a group that includes Coronaviruses, Dengue, HIV and Influenza) and catalyze fusion between the viral and host membranes, enabling the virus to insert its genetic material into the host cell. Given the importance of these biomolecules, this work presents a centralized database containing the most relevant information on viral fusion proteins, available through a free-to-use web server accessible through the URL https://viralfp.bio.di.uminho.pt/. This web application contains several bioinformatic tools, such as Clustal sequence alignment and Weblogo, including as well a machine learning-based tool capable of predicting the location of fusion peptides (the component of fusion proteins that inserts into the host's cell membrane) within the fusion protein sequence. Given the crucial role of these proteins in viral infection, their importance as natural targets of our immune system and their potential as therapeutic targets, this web application aims to foster our ability to fight pathogenic viruses.

Going virtual and going wide: comparing Team-Based Learning in-class versus online and across disciplines

Team-based learning (TBL) is an active learning pedagogy developed for in-class sessions and based on the collaborative work of small groups of students. The increasing push to online and blended learning has enhanced the need to expand this pedagogy to a virtual environment, but little evidence has been produced on how students accept online synchronous sessions of TBL. The purpose of this study, that relies on 427 responses, is to present a comparative perspective of traditional in-class versus adapted fully synchronous online TBL and across different disciplinary fields. Students of two different academic years and different programs were surveyed for their acceptance of TBL. They were invited to answer closed-ended questions focused on their engagement in all TBL learning process and the final outcomes provided. Results obtained from this unique comparative study revealed a wide approval of TBL, regardless of the environment (online or in-class TBL sessions), scientific area of courses and student gender. The acceptance of fully online TBL sessions, in a similar way as traditional in-class sessions, could be a rationale for giving more use to the 'virtual' context. Other results corroborated previous researches on TBL, such the need of student awareness of TBL benefits to get more engaged in the process or the impact of student activities overload on the TBL process. Implications are informative for pedagogical practice.

Knowledge and Use of PEP and PrEP Among Key Populations Tested in Community Centers in Portugal

Background: Pre-exposure prophylaxis (PrEP) and post-exposure prophylaxis (PEP) have been increasingly available in Europe. Due to the high burden of HIV in key populations, these could benefit from their use. In 2016, in Portugal, an open, non-interval, prospective cohort study was established in a network of 26 community-based voluntary HIV/STI counseling and testing centers. Data collected included questions on PEP and PrEP knowledge and use. We aimed to estimate the proportion of PEP and PrEP knowledge and its use among key populations, visiting the centers between 2016 and 2019. Method and results: Individuals who self-identify as being among at least one key population for HIV, men who have sex with men (MSM), people who inject drugs (PWID), sex workers (SW), migrants, and male-to-female transgender individuals (MTF), responded to questions on PEP and PrEP knowledge and use while waiting for their test results between 2016 and 2019 (n = 12,893 for PEP; n = 10,973 for PrEP). Reported knowledge was low in all key populations for both tools: 15.7% of respondents reported knowing about PEP and 10.9% about PrEP over the course of 4 years. PEP was used by 1.8% and PrEP by 0.4% of the respondents, MSM being 88.9% of PrEP users, and 52.8% of PEP users. Multivariate logistic regression showed multiple factors associated with knowing the tools, including age, education, country of birth, gender, year of test, having a reactive HIV test in the same visit, reporting an STI or condomless sex in the last 12 months, and identifying with being MSM or SW. Conclusions: Knowledge and use of PEP and PrEP remain low among key populations in Portugal. The need remains to increase knowledge and use among those at risk for HIV infection.

Model-guided development of an evolutionarily stable yeast chassis

First-principle metabolic modelling holds potential for designing microbial chassis that are resilient against phenotype reversal due to adaptive mutations. Yet, the theory of model-based chassis design has rarely been put to rigorous experimental test. Here, we report the development of Saccharomyces cerevisiae chassis strains for dicarboxylic acid production using genome-scale metabolic modelling. The chassis strains, albeit geared for higher flux towards succinate, fumarate and malate, do not appreciably secrete these metabolites. As predicted by the model, introducing product-specific TCA cycle disruptions resulted in the secretion of the corresponding acid. Adaptive laboratory evolution further improved production of succinate and fumarate, demonstrating the evolutionary robustness of the engineered cells. In the case of malate, multi-omics analysis revealed a flux bypass at peroxisomal malate dehydrogenase that was missing in the yeast metabolic model. In all three cases, flux balance analysis integrating transcriptomics, proteomics and metabolomics data confirmed the flux re-routing predicted by the model. Taken together, our modelling and experimental results have implications for the computer-aided design of microbial cell factories.

Encapsulation of Pseudomonas libanensis in alginate beads to sustain bacterial viability and inoculation of Vigna unguiculata under drought stress

Conventional agricultural practices based on the application of synthetic fertilizers are increasingly considered as unsustainable. Under a forecasted scenario of drought for the next decades, there is a global demand for innovative and sustainable approaches to ameliorate plant performance. Here, encapsulating beneficial microbes (BMs) to promote plant growth is gaining attention. This study evaluates bacterial encapsulation using polymeric beads of alginate, testing the survival of Pseudomonas libanensis TR1 stored up to 90 days. Produced beads were subjected to different treatments (fresh, air-dried and pulverized), which resulted in a variable size range (1200-860 µm). After storage, bacterial viability was maintained, and air-dried beads displayed a higher number of colony-forming units (2 × 107). Then, a glasshouse experiment investigated the drought resistance (plant growth, biomass, and photosynthetic responses) of Vigna unguiculata plants inoculated with these alginate beads. After 10 days of complete water restriction, turgidity and relative water content of V. unguiculata were still high under drought stress (> 80%). Leaf and root growth and biomass did not evidence significant changes after water restriction even after P. libanensis inoculation. Plant photosynthetic parameters (stomatal conductance, net photosynthetic rate, leaf CO2 concentration, or F v'/F m') were slightly affected due to inoculation but the level of stress-induced minimal plant responses. In our experiment, water restriction might have been insufficient to downregulate photosynthetic efficiency and reduce plant growth, limiting our understanding of the role of P. libanensis inoculation in alleviating drought stress in V. unguiculata, but highlighting the important relationship between the stress level and agricultural benefits of using encapsulated BMs.

Provision of Preexposure Prophylaxis at the Portuguese National Health Service and Uptake in the Lisbon Cohort of Men Who Have Sex with Men

We aimed to study the uptake of preexposure prophylaxis (PrEP) before and after its implementation in the Portuguese National Health Service (PNHS) among men who have sex with men (MSM). We studied 6164 participants in the Lisbon Cohort of MSM who participated between March 2014 and July 2019. 198 participants (3.2%) reported having recently used PrEP. Approximately one-third started PrEP after its implementation. PrEP uptake increased from 0.15% in 2014 to 5.36% in 2019. In their first use, 86 participants (70.5%) used it daily. How PrEP was obtained varied according to the timing of the first use: prescribed by a physician in Portugal (11.1% before vs 68.8% after implementation) and online (40.7% before vs 14.1% after). We observed an increase in the uptake and in the prescription by a physician, particularly after its implementation in the PNHS representing a change to a more equitable and safer way of using PrEP.

POS0872 CLINICAL FEATURES AND OUTCOME OF 1054 PATIENTS WITH SYSTEMIC SCLEROSIS: AN ANALYSIS OF THE PORTUGUESE REUMA.PT REGISTRY FOR SCLERODERMA (REUMA.PT/SSC)

Background:

Systemic sclerosis (SSc) may present distinctive manifestations and survival in different ethnic and geographic groups.

Objectives:

To describe the clinical features, treatments, and survival of adult SSc patients registered in Reuma.pt/SSc.

Methods:

Demographic features, SSc subsets, fulfilment of classification criteria, clinical and immunologic characteristics, comorbidities, medication and deaths were reviewed. Survival was calculated for patients included in the registry within the first 2 years of diagnosis.

Results:

In total, 1054 patients were included, 87.5% female, mean age at diagnosis 52.7 ± 14.8 years. The most common subset was limited cutaneous (lc)SSc (56.3%), followed by diffuse cutaneous (dc)SSc (17.5%), preclinical SSc (13%), overlap syndrome (9.8%) and SSc sine scleroderma (3.3%). Raynaud’s phenomenon (93.4%) and skin thickening (76.9%) were the most observed manifestations. Gastrointestinal (62.8% vs 47.8%), pulmonary (59.5% vs 23%) and cardiac (12.8% vs 6.9%) involvement were significantly more prevalent in dcSSc compared to lcSSc (Table 1). 52.5% of patients were ACA positive and 21% anti-topoisomerase positive, with significant differences between lcSSc and dcSSc. One third of patients was treated with immunomodulators, 53.6% with vasodilators, 23% received glucocorticoids and 2.3% biologics.

During the median follow-up 12.4 years, 83 deaths (7.9%) were verified. The overall 1, 2 and 5 years survival was 98.0%, 96.8% and 92.6% respectively, without significant differences between lcSSc and dcSSc (Figure 1).

Conclusion:

Reuma.pt/SSc register is useful in routine patient monitoring and contributes to improve knowledge about this rare and complex disease. Clinical features of Portuguese SSc patients are similar to what has been described in other populations although the overall 5-year survival in recently diagnosed patients appears to be higher than previously reported.

Table 1.

Cumulative clinical and immunologic characteristics of Portuguese SSc patients

Clinical and immunologic featuresTotalN=1054Limited cutaneous SScN= 576 (56.3%)Diffuse cutaneous SScN=180 (17.5%)P valueSkin involvement – N(%) N=987688 (90.6)525 (90.7)180 (100)<0.01Skin thickening * – N (%) N= 962680 (76.9)512 (88.9)180 (100)<0.01Digital ulcers – N (%) N=970325(33.5)186 (34.7)4 (51.5)<0.01Raynaud’s Phenomenon – N (%) N=1010943 (93.4)539 (95.7)157 (92.4)0.06Musculoskeletal involvement – N(%) N=972346 (45.6)247 (42.7)99 (55)<0.01Cardiac involvement –N(%) – N=92471 (7.7)36 (6.9)19 (12.8)0.02Renal involvement –N(%) – N= 91717 (1.9)8!1.5)6 (4.1)0.07Gastrointestinal involvement - N(%) N=933508 (48.2)277 (47.8)113 (62.8)<0.01Pulmonary involvement – N(%) N=915261 (28.5)119 (23)88 (59.5)<0.01PAH – N(%) N= 87114 (1.6)10 (2)1 (0.7)0.23Intersticial lung disease – N(%) N=765218 (28.5)100 (22.7)75 (57.7)<0.01Antinuclear antibodies - N(%) N=1040934 (89.8)522 (90.2)154 (88.5)0.57Anti-centromere – N(%) N= 1027540 (52.6)383 (67.1)16 (9.5)<0.01Anti-Scl70 – N(%) N=1020214 (21)12 (3.3)104 (60.1)<0.01Anti-RNA polymerase III – N(%) N=71025 (3.5)12 (3.3)7 (5.6)0.38ComorbiditiesHypertension – N(%) N=431117 (27.1)76 (29.7)67 (20.7)0.1Hyperlipidemia – N(%) N=43171 (13.4)72 (12.2)24 (15.9)0.08Neoplasia – N(%) N=105429 (2.8)12 (2.1)7 (3.9)0.14

PDE-5 (phosdiasterase-5); PPIs (proton pump inhibitors); PAH-Pulmonary arterial hypertension confirmed by right heart catheterization. Immunomodulators includes Metothrexate, Leflunomide, Hydroxycloroquine; Azathioprine, Mycophenolate Mofetil and Cyclophosphamide; * Does not include sclerodactyly.

Figure 1.

Panel A - Survival in years from diagnosis of patients with SSc included in Reuma.pt in the first 2 years of disease (N=472). Panel B - survival according to SSc subset (lcSSc and dcSSC).

Disclosure of Interests:

None declared

Transferring Occlusal Anatomy from Worn Temporary Crowns to Zirconium Oxide Crowns

Purpose/Aim: To evaluate the accuracy of transferring the occlusal anatomy of provisional crowns to monolithic zirconium oxide crowns. Materials and Methods: From a prepared typodont-tooth (#36), ten CAD/CAM provisional polymethylethacrylate crowns were fabricated with the E4D system. Each provisional crown had its occlusion adjusted. They were scanned (E4D) and the .stl files of the crown preparations were merged with the files from the adjusted crowns (3 shape software) to produce ten polished monolithic zirconium oxide crowns. For comparison, provisional and monolithic zirconium oxide crowns were scanned (True-Definition scanner), the .stl files aligned, converted into a normalized 76x76-matrix, analyzed with ANOVA with repeated measures and Tukey's test. To generate deviation distribution tables and difference plots, .stl files (provisional crowns and monolithic zirconium oxide crowns) were merged with Geomagic software. Results: There were significant differences between provisional crowns and monolithic zirconium oxide crowns. The differences were manly in the fissure area. 86% of the calculated deviations were between + 0.06mm and - 0.04mm, 42.4% of all data points were within ± 0.022mm with a SD of 0.005mm. The main differences were in the fissures, requiring clinically none or only minimal occlusal adjustments for these zirconium oxide crowns.

NMRFinder: a novel method for 1D 1H-NMR metabolite annotation

INTRODUCTION

Methods for the automated and accurate identification of metabolites in 1D ¹H-NMR samples are crucial, but this is still an unsolved problem. Most available tools are mainly focused on metabolite quantification, thus limiting the number of metabolites that can be identified. Also, most only use reference spectra obtained under the same specific conditions of the target sample, limiting the use of available knowledge.

OBJECTIVES

The main goal of this work was to develop novel methods to perform metabolite annotation from 1D ¹H-NMR peaks with enhanced reliability, to aid the users in metabolite identification. An essential step was to construct a vast and up-do-date library of reference 1D ¹H-NMR peak lists collected under distinct experimental conditions.

METHODS

Three different algorithms were evaluated for their capacity to correctly annotate metabolites present in both synthetic and real samples and compared to publicly available tools. The best proposed method was evaluated in a plethora of scenarios, including missing references, missing peaks and peak shifts, to assess its annotation accuracy, precision and recall.

RESULTS

We gathered 1816 peak lists for 1387 different metabolites from several sources across different conditions for our reference library. A new method, NMRFinder, is proposed and allows matching 1D ¹H-NMR samples with all the reference peak lists in the library, regardless of acquisition conditions. Metabolites are scored according to the number of peaks matching the samples, how unique their peaks are in the library and how close the spectrum acquisition conditions are in relation to those of the samples. Results show a true positive rate of 0.984 when analysing computationally created samples, while 71.8% of the metabolites were annotated when analysing samples from previously identified public datasets.

CONCLUSION

NMRFinder performs metabolite annotation reliably and outperforms previous methods, being of great value in helping the user to ultimately identify metabolites. It is implemented in the R package specmine.

Transcontinental Dissemination of the L2b/D-Da Recombinant Chlamydia trachomatis Lymphogranuloma venereum (LGV) Strain: Need of Broad Multi-Country Molecular Surveillance

Previously, we identified a Chlamydia trachomatis lymphogranuloma venereum (LGV) recombinant strain possessing a non-LGV ompA genotype. Here, culture-independent genome sequencing confirms its circulation in Europe, Middle East, and North America, and unveils emergence of antibiotic resistance. Broad surveillance is needed.

MEWpy: A Computational Strain Optimization Workbench in Python

Summary

Metabolic Engineering aims to favour the overproduction of native, as well as non-native, metabolites by modifying or extending the cellular processes of a specific organism. In this context, Computational Strain Optimization (CSO) plays a relevant role by putting forward mathematical approaches able to identify potential metabolic modifications to achieve the defined production goals. We present MEWpy, a Python workbench for metabolic engineering, which covers a wide range of metabolic and regulatory modelling approaches, as well as phenotype simulation and CSO algorithms.

Availability and implementation

MEWpy can be installed from PyPi (pip install mewpy), the source code being available at https://github.com/BioSystemsUM/mewpy under the GPL license.

Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.

Learnings from a decade experience of a community-based HIV and STI testing and counseling center in Lisbon – Portugal

Background

In the context of an increased number of men who have sex with men (MSM) being diagnosed with HIV infection in many European countries, it became a matter of maximum priority to consider new strategies to monitor and tackle the epidemic. In this scenario, CheckpointLX and a cohort study were projected as a sexual health and research center oriented towards MSM in Portugal.

Methods

In 2011 CheckpointLX was launched in Lisbon as a community-based center for anonymous and free rapid HIV and other sexually transmitted diseases (STI) testing with a strong component of counseling for sexual health, specially targeted at MSM. In the case of a reactive test, a medical appointment is proposed and scheduled. The Lisbon Cohort of MSM is an ongoing observational prospective study conducted at CheckpointLX. Collected data include sexual behaviors, preventive strategies, and history of STI.

Results

Until July 2019, 7,351 HIV-negative MSM chose to participate in the Lisbon Cohort of MSM. 3,523 had at least one follow-up visit. The median age was 29 (IQR 25 - 37), and 27% were born in a foreign country, including 15% born in Africa and South America. HIV incidence increased up until 2015, reaching 5 reactive tests per 100 tested annually. There has been a sustained decrease in HIV observed since 2015. The use of HIV prevention tools was provided: 31% used condoms consistently, 3% reported PrEP, and 4% PEP.

Conclusions

A decade since the beginning of this experience shows positive results, such as increased access and testing. We promoted a stigma-free service that facilitated MSM's and other key population's access to sexual health care. We contribute unique data for assisting in the monitoring of the Dublin Declaration in Portugal.

Early ART in Acute HIV-1 Infection: Impact on the B-Cell Compartment

HIV-1 infection induces B cell defects, not fully recovered upon antiretroviral therapy (ART). Acute infection and the early start of ART provide unique settings to address the impact of HIV on the B cell compartment. We took advantage of a cohort of 21 seroconverters, grouped according to the presence of severe manifestations likely mediated by antibodies or immune complexes, such as Guillain-Barré syndrome and autoimmune thrombocytopenic purpura, with a follow-up of 8 weeks upon effective ART. We combined B and T cell phenotyping with serum immunoglobulin level measurement and quantification of sj-KRECs and ΔB to estimate bone marrow output and peripheral proliferative history of B cells, respectively. We observed marked B cell disturbances, notably a significant expansion of cells expressing low levels of CD21, in parallel with markers of both impaired bone marrow output and increased peripheral B cell proliferation. This B cell dysregulation is likely to contribute to the severe immune-mediated conditions, as attested by the higher serum IgG and the reduced levels of sj-KRECs with increased ΔB in these individuals as compared to those patients with mild disease. Nevertheless, upon starting ART, the dynamic of B cell recovery was not distinct in the two groups, featuring both persistent alterations by week 8. Overall, we showed for the first time that acute HIV-1 infection is associated with decreased bone marrow B cell output assessed by sj-KRECs. Our study emphasizes the need to intervene in both bone marrow and peripheral responses to facilitate B cell recovery during acute HIV-1 infection.

Predicting promoters in phage genomes using PhagePromoter

SUMMARY

The growing interest in phages as antibacterial agents has led to an increase in the number of sequenced phage genomes, increasing the need for intuitive bioinformatics tools for performing genome annotation. The identification of phage promoters is indeed the most difficult step of this process. Due to the lack of online tools for phage promoter prediction, we developed PhagePromoter, a tool for locating promoters in phage genomes, using machine learning methods. This is the first online tool for predicting promoters that uses phage promoter data and the first to identify both host and phage promoters with different motifs.

AVAILABILITY AND IMPLEMENTATION

This tool was integrated in the Galaxy framework and it is available online at: https://bit.ly/2Dfebfv.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Different guidelines for pre-exposure prophylaxis (PrEP) eligibility estimate HIV risk differently: an incidence study in a cohort of HIV-negative men who have sex with men, Portugal, 2014-2018

IntroductionGuidelines for pre-exposure prophylaxis (PrEP) provide criteria to identify individuals at higher risk of HIV infection. We compared the ability to predict HIV seroconversion of four guidelines: the World Health Organization (WHO), the United States Public Health Service and Centers for Disease Control and Prevention (US CDC), the European AIDS Clinical Society (EACS) and the Portuguese National Health Service (PNHS).AimWe aimed to measure the association between guideline-specific eligibility and HIV seroconversion.MethodsWe studied 1,254 participants from the Lisbon Cohort of men who have sex with men with at least two evaluations between March 2014 and March 2018, corresponding to 1,724.54 person-years (PY) of follow-up. We calculated incidence rates (IR) according to each guideline eligibility definition and incident rate ratios (IRR) to test the association between eligibility at baseline and HIV seroconversion.ResultsWe found 28 incident cases (IR: 1.62/100 PY; 95% confidence interval (CI) 1.12-2.35). Guidelines' sensitivity varied from 60.7% (EACS) to 85.7% (PNHS) and specificity varied from 31.8% (US CDC) to 51.5% (EACS). IR was highest among those defined as eligible by the PNHS guideline (2.46/100 PY; IRR = 4.61; 95% CI: 1.60-13.27) and lowest for the WHO guideline (1.89/100 PY; IRR = 1.52; 95% CI: 0.69-3.35).ConclusionsBeing identified as eligible for PrEP was associated with a higher risk of infection. The magnitude of risk varied according to the guideline used. However, the number of HIV infections identified among ineligible participants highlights the potential for missing people who need PrEP.