Accurate consistency-based MSA reducing the memory footprint.

BACKGROUND AND OBJECTIVE: The emergence of Next-Generation sequencing has created a push for faster and more accurate multiple sequence alignment tools. The growing number of sequences and their longer sizes, which require the use of increased system resources and produce less accurate results, are heavily challenging to these applications. Consistency-based methods have the most intensive CPU and memory usage requirements. We hypothesize that library reductions can enhance the scalability and performance of consistency-based multiple sequence alignment tools; however, we have previously shown a noticeable impact on the accuracy when extreme reductions were performed. METHODS: In this study, we propose Matrix-Based T-Coffee, a consistency-based method that uses library reductions in conjunction with a complementary objective function. The proposed method, implemented in T-Coffee, can mitigate the accuracy loss caused by low memory resources. RESULTS: The use of a complementary objective function with a library reduction of ≥30% improved the accuracy of T-Coffee. Interestingly, ≥50% library reduction achieved lower execution times and better overall scalability. CONCLUSIONS: Matrix-Based T-Coffee benefits from accurate alignments while achieving better scalability. This leads to a reduction in memory footprint and execution time. In addition, these enhancements could be applied to other aligners based on consistency.

Targeting of the CD80/86 proinflammatory axis as a therapeutic strategy to prevent severe COVID-19.

An excessive immune response known as cytokine storm is the hallmark of severe COVID-19. The cause of this cytokine rampage is yet not known. Based on recent epidemiological evidence, we hypothesized that CD80/86 signaling is essential for this hyperinflammation, and that blocking this proinflammatory axis could be an effective therapeutic approach to protect against severe COVID-19. Here we provide exploratory evidence that abatacept, a drug that blocks CD80/86 co-stimulation, produces changes at the systemic level that are highly antagonistic of the proinflammatory processes elicited by COVID-19. Using RNA-seq from blood samples from a longitudinal cohort of n = 38 rheumatic patients treated with abatacept, we determined the immunological processes that are significantly regulated by this treatment. We then analyzed available blood RNA-seq from two COVID19 patient cohorts, a very early cohort from the epicenter of the pandemic in China (n = 3 COVID-19 cases and n = 3 controls), and a recent and larger cohort from the USA (n = 49 severe and n = 51 mild COVD-19 patients). We found a highly significant antagonism between SARS-CoV-2 infection and COVID-19 severity with the systemic response to abatacept. Analysis of previous single-cell RNA-seq data from bronchoalveolar lavage fluid from mild and severe COVID-19 patients and controls, reinforce the implication of the CD80/86 proinflammatory axis. Our functional results further support abatacept as a candidate therapeutic approach to prevent severe COVID-19.

Interactions between rheumatoid arthritis antibodies are associated with the response to anti-tumor necrosis factor therapy.

BACKGROUND: Blocking of the Tumor Necrosis Factor (TNF) activity is a successful therapeutic approach for 50-60% of rheumatoid arthritis (RA) patients. However, there are yet no biomarkers to stratify patients for anti-TNF therapy. Rheumatoid factor (RF) and anti-cyclic-citrullinated antibodies (anti-CCP) have been evaluated as biomarkers of response but the results have shown limited consistency. Anti-carbamylated protein (anti-CarP) and anti-peptidylarginine deiminase type 4 (anti-PAD4) antibodies have been much less studied. Despite being linked to common immune processes, the interaction between these markers has not been evaluated yet. Our aim was to analyze the interaction between these four antibodies in relation to the response to anti-TNF therapy. METHODS: For this objective, a prospective cohort of n = 80 RA patients starting anti-TNF therapy was recruited. Serum determinations at baseline were performed for RF, anti-CCP, anti-CarP and anti-PAD4 antibodies using enzyme-linked immunosorbent assays (ELISA). The clinical response to anti-TNF therapy was determined at week 12 using the change in DAS28 score. Association was performed using multivariate linear regression adjusting for baseline DAS28, sex and age. RESULTS: The interaction between pairs of antibodies was tested by the addition of an interaction term. We found two highly significant antibody interactions associated with treatment response: anti-CarP with anti-PAD4 (p = 0.0062), and anti-CCP with RF (p = 0.00068). The latter antibody interaction was replicated in an independent retrospective cohort of RA patients (n = 199, p = 0.04). CONCLUSIONS: The results of this study suggest that antibody interaction effects are important factors in the response to anti-TNF therapy in RA.

Incidence of COVID-19 in a cohort of adult and paediatric patients with rheumatic diseases treated with targeted biologic and synthetic disease-modifying anti-rheumatic drugs.

Objectives: To investigate the incidence of COVID-19 in a cohort of adult and paediatric patients with rheumatic diseases receiving targeted biologic and synthetic disease modifying anti-rheumatic drugs (tDMARDs) and to explore the possible effect of these treatments in the clinical expression of COVID-19. Methods: A cross-sectional study comprising of a telephone survey and electronic health records review was performed including all adult and paediatric patients with rheumatic diseases treated with tDMARDs in a large rheumatology tertiary centre in Barcelona, Spain. Demographics, disease activity, COVID-19 related symptoms and contact history data were obtained from the start of the 2020 pandemic. Cumulative incidence of confirmed cases (SARS-CoV-2 positive PCR test) was compared to the population estimates for the same city districts from a governmental COVID-19 health database. Suspected cases were defined following WHO criteria and compared to those without compatible symptoms. Results: 959 patients with rheumatic diseases treated with tDMARDs were included. We identified 11 confirmed SARS-CoV-2 positive cases in the adult cohort and no confirmed positive cases in the paediatric cohort. COVID-19 incidence rates of the rheumatic patient cohort were very similar to that of the general population [(0.48% (95% CI 0.09 to 0.87%)] and [0.58% (95% CI 0.56 to 0.60%)], respectively. We found significant differences in tDMARDs proportions between the suspected and non-suspected cases (p=0.002). Conclusion: Adult and paediatric patients with rheumatic diseases on tDMARDs do not seem to present a higher risk of COVID-19 or a more severe disease outcome when compared to general population.

Scalable Consistency in T-Coffee Through Apache Spark and Cassandra Database.

Next-generation sequencing, also known as high-throughput sequencing, has increased the volume of genetic data processed by sequencers. In the bioinformatic scientific area, highly rated multiple sequence alignment tools, such as MAFFT, ProbCons, and T-Coffee (TC), use the probabilistic consistency as a prior step to the progressive alignment stage to improve the final accuracy. However, such methods are severely limited by the memory required to store the consistency information. Big data processing and persistence techniques are used to manage and store the huge amount of information that is generated. Although these techniques have significant advantages, few biological applications have adopted them. In this article, a novel approach named big data tree-based consistency objective function for alignment evaluation (BDT-Coffee) is presented. BDT-Coffee is based on the integration of consistency information through Cassandra database in TC, previously generated by the MapReduce processing paradigm, to enable large data sets to be processed with the aim of improving the performance and scalability of the original algorithm.