Biology subjects

Rocha, M.

Publications and source records attributed to Rocha, M..

At least 19 recordsLinked to original sources

bioRxiv2026

A primary human muscle cell-based assay for detecting myasthenia gravis autoantibody binding and assessing AChR cluster impairment

Myasthenia gravis (MG) is an autoimmune disease caused by pathogenic autoantibodies against proteins at the neuromuscular junction (NMJ). The diagnosis and clinical management of MG patients largely relies on the detection of antigen-specific autoantibodies targeting acetylcholine receptor (AChR) or muscle-specific kinase (MuSK). Yet a subset of patients remains seronegative for known MG autoantibodies, highlighting a critical need for alternative approaches to identify pathogenic NMJ antibodies. We established a new human in vitro model of the NMJ based on primary human muscle cells that recapitulates key features of the NMJ: differentiation to myotubes, expression of key NMJ proteins and formation of postsynaptic AChR clusters in response to agrin stimulation. The model allows new insights into myogenesis and genetic muscle diseases, and the new muscle cell-based assay (CBA) detected autoantibodies in sera from patients with AChR- and MuSK-positive MG with 96.43% sensitivity and 100% specificity, while healthy control sera showed no reactivity. Incubation with patient sera significantly reduced AChR clustering compared to controls, demonstrating functional pathogenic effects. Thus, we established a physiologically relevant human NMJ model that enables detection and functional characterization of neuromuscular autoantibodies. This novel approach addresses a key limitation of current antigen-specific diagnostics and provides a method for improved detection and characterization of MG antibodies, independent of antigen specificity. One Sentence SummaryWe established a postsynaptic human in vitro neuromuscular junction model to assess binding and pathogenicity of MG autoantibodies. Key messagesO_ST_ABSWhat is already known on this topic?C_ST_ABSCurrent diagnosis of myasthenia gravis (MG) relies largely on the detection of antigen-specific autoantibodies against AChR and MuSK, leaving a clinically relevant subset of patients seronegative. What are the new findings?We established a physiologically relevant human in vitro neuromuscular junction model based on primary human muscle cells and developed a novel muscle cell-based assay (CBA) for the detection of neuromuscular autoantibodies. How might this impact on clinical practice or future developments?The CBA detected autoantibodies in patients with AChR- or MuSK-positive MG with high sensitivity and specificity and demonstrated their functional pathogenic effects on AChR clustering. This antigen-independent approach may improve the detection and functional characterization of MG autoantibodies, particularly in patients who are seronegative in current diagnostic assays. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/743478v1_ufig1.gif" ALT="Figure 1000"> View larger version (38K): org.highwire.dtl.DTLVardef@c2e1fforg.highwire.dtl.DTLVardef@8336fforg.highwire.dtl.DTLVardef@8fa2b0org.highwire.dtl.DTLVardef@1d94e_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

Time-frequency EEG markers of word boundaries in speech production

Speech production requires orchestration of multiple brain systems, including cortical and subcortical areas that support the unfolding of the spoken message across hierarchical linguistic levels, such as phonemes, syllables, words or phrases. Transitions between levels are critical for fluent speech, yet the neural dynamics of, for example, syllable-level and word-level transitions remain unknown. In this electroencephalography (EEG) study, we use time-frequency analysis and source localization to determine differences associated with word-boundary vs. within-word syllable transitions. To this end, pseudoword pairs comprising six consonant-vowel (CV) syllables with different word-boundary positions were used. Fluent human adults produced the utterances at the rhythm of a learned visual metronome (i.e., syllable-by-syllable), such that each syllable was uttered at matching times independently of its relative word position. Accordingly, a target syllable could be either a within-word syllabic transition or a between-word transition, while other linguistic properties, including articulation, stress pattern, co-articulation or prosody, were matched. EEG time-frequency analyses of neural sources successfully revealed sensitivity to hierarchical structure. Neural sources in left and right inferior frontal lobes, as well as left superior temporal lobe were differentially recruited when producing the same exact syllables, in the same exact utterance position, but under different word boundary contexts. A right inferior frontal source showed a robust time-frequency modulation in word transitions that included elevated event-related synchronization in the theta and beta range. Interestingly, despite our efforts to control speech pace across conditions using metronome-based guidance, small, albeit significant timing delays emerged, confirming higher cognitive demands at word boundaries.

neuroscience↗

bioRxiv2025

GRIDGEN: Guided Region Identification based on Density of GENes - a transcript density-based approach to characterize tissues by spatial transcriptomics

Spatial omics brought unprecedented power to study biological processes within tissues while preserving spatial context and morphology. Most spatial proteomics and transcriptomics analyses methods are cell-centric, relying on cell segmentation to identify and characterize individual cells before downstream tasks. However, certain biological questions may be better addressed using cell-free approaches, which also eliminate unnecessary computations when cell segmentation is not essential. To address this need, we developed GRIDGENE (Guided Region Identification based on Density of GENEs), an approach for defining regions of interest based on transcript density. GRIDGENE enables the identification of biologically relevant tissue compartments, including interfaces between regions, phenotype-enriched areas, and zones defined by specific gene signatures, supporting analyses such as pathway enrichment. We demonstrated the utility of GRIDGENE by applying it to spatial transcriptomics data from CosMx and Xenium platforms in colorectal cancer (CRC) samples. By bypassing cell segmentation, our approach enables flexible analysis of spatial omics data, supporting the study of biological processes across diverse tissue structures and microenvironments. Nevertheless, GRIDGENE can be easily integrated with cell segmentation strategies for complementary analyses. GRIDGENE thus broadens the analytical toolkit for spatial omics, enabling both cell-free and cell-based insights.

bioinformatics↗

bioRxiv2024

Harnessing Random Peptide Mixtures to Overcome Multidrug-Resistant Fungal Infections

Invasive fungal infections are associated with high mortality and are increasingly difficult to treat due to a limited antifungal arsenal and the rapid emergence of drug resistance. Novel therapeutic strategies that combine potent antifungal activity, low host toxicity, in vivo stability, and a reduced propensity for resistance development are urgently needed. Antimicrobial peptides (AMPs) stand out as a promising class of compounds to combat antimicrobial resistance. Leveraging the unique properties of AMPs, we previously developed a novel approach to synthesize random peptide mixtures (RPMs) with robust bactericidal activity against drug-resistant bacteria. Here, we evaluate the antifungal potential of RPMs and demonstrate species-dependent, broad-spectrum activity of FK20 (L-phenylalanine-L-lysine, 20-mer) against major human fungal pathogens, including Candida spp., Cryptococcus neoformans, and Aspergillus fumigatus, with particularly high potency against the multidrug-resistant pathogen Candida auris. Mechanistic analyses revealed rapid membrane and cell wall disruption accompanied by intracellular penetration, consistent with membrane-active antifungal activity. Importantly, experimental evolution assays demonstrated a markedly reduced capacity for resistance development in C. auris. FK20 inhibited biofilm formation and displayed substantial activity against mature, pre-formed biofilms, both alone and synergistically in combination with caspofungin. Finally, FK20 showed significant therapeutic efficacy in a murine model of systemic candidiasis. Collectively, these findings establish RPMs as a versatile antifungal platform with broad-spectrum activity, biofilm efficacy, and a low resistance footprint, highlighting their promise as a novel therapeutic strategy against drug-resistant fungal infections. ImportanceThe rising prevalence of invasive fungal infections, particularly among immunocompromised individuals, has become a critical public health concern. However, antifungal drug development has not kept pace with this growing need, and treatment options remain limited to a small number of drug classes. The emergence of multidrug-resistant fungal pathogens, such as Candida auris, further exacerbates this crisis by reducing the efficacy of existing therapeutics and increasing the risk of treatment failure. In this study, we evaluate the antifungal potential of FK20, a random peptide mixture (RPM) composed of L-phenylalanine and L-lysine. FK20 displays potent activity against C. auris and other clinically relevant human fungal pathogens, impairs biofilm formation, and exhibits synergy with caspofungin. Importantly, FK20 limits the emergence of resistance and demonstrates therapeutic efficacy in a murine model of systemic candidiasis. These findings establish RPMs as a promising new class of antifungals with broad-spectrum activity and clinical potential against drug-resistant fungal infections.

microbiology↗

bioRxiv2024

PENGUIN: A rapid and efficient image preprocessing tool for multiplexed spatial proteomics

Multiplex spatial proteomic methodologies can provide a unique perspective on the molecular and cellular composition of complex biological systems. Several challenges are associated to the analysis of imaging data, in particular regarding the normalization of signal-to-noise ratios across images and background noise subtraction. However, straightforward and user-friendly solutions for denoising multiplex imaging data that are applicable to large datasets are still lacking. We have developed PENGUIN -Percentile Normalization GUI Image deNoising: a rapid and efficient image preprocessing tool for multiplexed spatial proteomics. In comparison to existing approaches, PENGUIN stands out by eliminating the need for manual annotation or machine learning model training. It effectively preserves signal intensity differences and reduces noise, thereby enhancing downstream tasks like cell segmentation and phenotyping. PENGUINs simplicity, speed, and user-friendly interface, deployed both as script and as a Jupyter notebook, facilitate parameter testing and image processing. We illustrate the effectiveness of PENGUIN by comparing it with conventional image processing techniques and solutions tailored for multiplex imaging data. This comparison underscores PENGUINs capability to produce high-quality imaging data efficiently and consistently.

bioinformatics↗

bioRxiv2024

DeepMol: An Automated Machine and Deep Learning Framework for Computational Chemistry

The domain of computational chemistry has experienced a significant evolution due to the introduction of Machine Learning (ML) technologies. Despite its potential to revolutionize the field, researchers are often encumbered by obstacles, such as the complexity of selecting optimal algorithms, the automation of data pre-processing steps, the necessity for adaptive feature engineering, and the assurance of model performance consistency across different datasets. Addressing these issues head-on, DeepMol stands out as an Automated ML (AutoML) tool by automating critical steps of the ML pipeline. DeepMol rapidly and automatically identifies the most effective data representation, pre-processing methods and model configurations for a specific molecular property/activity prediction problem. On 22 benchmark datasets, DeepMol obtained competitive pipelines compared with those requiring time-consuming feature engineering, model design and selection processes. As one of the first AutoML tools specifically developed for the computational chemistry domain, DeepMol stands out with its open-source code, in-depth tutorials, detailed documentation, and examples of real-world applications, all available at https://github.com/BioSystemsUM/DeepMol and https://deepmol.readthedocs.io/en/latest/. By introducing AutoML as a ground-breaking feature in computational chemistry, DeepMol establishes itself as the pioneering state-of-the-art tool in the field.

bioinformatics↗

bioRxiv2024

Aspergillus dsRNA virus drives fungal fitness and pathogenicity in the mammalian host

Fungal pathogens pose a significant threat to global health. Aspergillus fumigatus accounts for approximately 65% of all invasive fungal infections in humans, with mortality rates from invasive aspergillosis reaching nearly 50%. Mycoviruses, viruses that infect fungi, can modulate fungal virulence in plant pathogenic fungi, leading to either hypovirulence or hypervirulence. However, their impact on fungal pathogenesis in mammals has remained largely unexplored. Here, utilizing an A. fumigatus strain naturally infected with Aspergillus fumigatus polymycovirus-1M (AfuPmV-1M), we found that the mycovirus confers a significant survival advantage to the fungus under conditions of oxidative stress, heat stress, and within the murine lung. Thus, AfuPmV-1M modulates fungal fitness, resulting in increased virulence and the progression of exacerbated fungal disease. Moreover, antiviral treatment reverses the virus-mediated increase in virulence, representing a promising "antipathogenicity" therapy against virus-bearing pathogenic fungi. Collectively, these findings reveal that mycoviruses act as pivotalbackseat drivers in human fungal diseases, underscoring significant clinical implications and offering promising avenues for novel therapeutic strategies.

microbiology↗

bioRxiv2024

A diel multi-tissue genome-scale metabolic model of Vitis vinifera

Vitis vinifera, also known as grapevine, is widely cultivated and commercialized, particularly to produce wine. As wine quality is directly linked to fruit quality, studying grapevine metabolism is important to understand the processes underlying grape composition. Genome-scale metabolic models (GSMMs) have been used for the study of plant metabolism and advances have been made, allowing the integration of omics datasets with GSMMs. On the other hand, Machine learning (ML) has been used to analyze omics data, and while the combination of ML with GSMMs has shown promising results, it is still scarcely used to study plants. Here, the first GSSM of V. vinifera was reconstructed and validated, comprising 7199 genes, 5399 reactions, and 5141 metabolites across 8 compartments. Tissue-specific models for stem, leaf, and berry of the Cabernet Sauvignon cultivar were generated from the original model, through the integration of RNA-Seq data. These models have been merged into diel multi-tissue models to study the interactions between tissues at light and dark phases. The potential of combining ML with GSMMs was explored by using ML to analyze the fluxomics data generated by green and mature grape GSMMs, helping to understand the factors influencing grape quality at different developmental stages.

bioinformatics↗

bioRxiv2023

High capacity of Brazilian Aedes aegypti populations to transmit a locally circulating lineage of Chikungunya virus

The global incidence of chikungunya has surged in recent decades, with South America, particularly Brazil, experiencing devastating outbreaks. The primary vector for transmitting CHIKV in urban areas is the mosquito species Aedes aegypti, which is very abundant in Brazil. However, little is known about the impact of locally circulating CHIKV genotypes and specific combinations of mosquito populations on vector competence. In this study, we analyzed and compared the infectivity and transmissibility of a recently isolated CHIKV-ECSA lineage from Brazil among four Ae. aegypti populations collected from different regions of the country. When exposed to CHIKV-infected mice for blood feeding, all mosquito populations showed high infection rates and dissemination efficiency. Moreover, using a mouse model to assess transmission rates in a manner that better mirrors natural cycles, we observed that these populations exhibit highly efficient transmission rates of CHIKV-ECSA. Our findings underscore the robust capability of Brazilian Ae. aegypti populations to transmit the locally circulating CHIKV-ECSA lineage, potentially explaining its higher prevalence compared to the Asian lineage also introduced in Brazil. Author SummaryChikungunya virus (CHIKV) is an alphavirus transmitted by mosquitoes, gaining attention due to its rapid global spread and public health impact. Initially isolated in Tanzania in 1952, it was confined to Africa, Asia, and the Indian subcontinent. Local transmission of CHIKV in the Americas began in December 2013, leading to a 2014 epidemic. The Aedes aegypti mosquito, prevalent in Brazil, is suspected as the primary vector. Yet, our understanding of how different mosquito populations and virus lineages impact spread is limited. In this Brazilian study, we collected mosquitoes from four regions, exposing them to the local African ECSA lineage of CHIKV. Remarkably, all mosquito populations exhibited high infection rates and efficient virus transmission to vertebrate hosts. This research sheds light on why the ECSA chikungunya lineage is spreading rapidly in Brazil. The Brazilian Ae. aegypti mosquitoes appear to possess exceptional capabilities in transmitting African ECSA lineage of CHIKV, potentially contributing to its rapid dissemination within the country and neighboring regions. Comprehending these dynamics is vital for developing strategies to control and mitigate the impact of chikungunya in affected areas.

immunology↗

bioRxiv2022

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 on performance. Drug features appeared to be more predictive of drug response. Molecular fingerprint-based drug representations performed slightly better than learned representations, and gene expression data of cancer or drug response-specific genes also improved performance. In general, fully connected feature-encoding subnetworks outperformed other architectures, with DL outperforming other ML methods. 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. Author summaryCancer therapies often fail because tumor cells become resistant to treatment. One way to overcome resistance is by treating patients with a combination of two or more drugs. Some combinations may be more effective than when considering individual drug effects, a phenomenon called drug synergy. Computational drug synergy prediction methods can help to identify new, clinically relevant drug combinations. In this study, we developed several deep learning models for drug synergy prediction. We examined the effect of using different types of deep learning architectures, and different ways of representing drugs and cancer cell lines. We explored the use of biological prior knowledge to select relevant cell line features, and also tested data-driven feature reduction methods. We tested both precomputed drug features and deep learning methods that can directly learn features from raw representations of molecules. We also evaluated whether including genomic features, in addition to gene expression data, improves the predictive performance of the models. Through these experiments, we were able to identify strategies that will help guide the development of new deep learning models for drug synergy prediction in the future.

bioinformatics↗

bioRxiv2021

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. Author summaryGenome-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.

systems biology↗

bioRxiv2021

RNAP II produces capped 18S and 25S ribosomal RNAs resistant to 5-monophosphate dependent processive 5-3 exonuclease in polymerase switched Saccharomyces cerevisiae

We have previously found in the pathogenic yeast Candida albicans, 18S and 25S ribosomal RNA components, containing more than one phosphate on their 5-end, resistant to 5-monophosphate requiring 5[->]3" exonuclease. Several lines of evidence pointed to RNAP II as the enzyme producing them. We now show in Saccharomyces cerevisiae, permanently switched to RNAP II, due to deletion part of RNAP I upstream activator alone or in combination with deletion of one component of RNAP I itself, the production of such 18S and 25S rRNAs. They contain multiple phosphates at their 5-end and an anti-cap specific antibody binds to them indicating capping of these molecules. These molecules are found in RNA isolated from nuclei, therefore are unlikely to be capped in the cytoplasm. This would be unlike recapping of decapped mRNAs which occurs in the cytoplasm. Our data confirm the existence of such molecules and firmly establish RNA II playing a role in their production. The fact that we see these molecules in wild type Saccharomyces cerevisiae indicates that they are not only a result of mutations but are part of the cells physiology. This adds another way RNAP II is involved in ribosome production in addition to their role in the production of ribosome associated proteins.

molecular biology↗

bioRxiv2021

Notch controls the cell cycle to define leader versus follower identities during collective cell migration.

Coordination of cell proliferation and migration is fundamental for life, and its dysregulation has catastrophic consequences, as cancer. How cell cycle progression affects migration, and vice-versa, remains largely unknown. We address these questions by combining in silico modelling and in vivo experimentation in the zebrafish Trunk Neural Crest (TNC). TNC migrate collectively, forming chains with a leader cell directing the movement of trailing followers. We show that the acquisition of migratory identity is autonomously controlled by Notch signalling in TNC. High Notch activity defines leaders, while low Notch determines followers. Moreover, cell cycle progression is required for TNC migration and is regulated by Notch. Cells with low Notch activity stay longer in G1 and become followers, while leaders with high Notch activity quickly undergo G1/S transition and remain in S-phase longer. We propose that migratory behaviours are defined through the interaction of Notch signalling and cell cycle progression.

developmental biology↗

bioRxiv2021

iEC7871 Quercus suber model: the first multi-tissue diel cycle genome-scale metabolic model of a woody tree

In the last decade, genome-scale metabolic models have been increasingly used to study plant metabolic behavior 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 first of a woody plant. 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 analyze the pathways associated with the synthesis of suberin monomers. Nevertheless, the models developed in this work can provide insights into other aspects of the metabolism of Q. suber, such as its secondary metabolism and cork formation.

systems biology↗

bioRxiv2021

BioISO: an objective-oriented application for assisting the curation of genome-scale metabolic models

As the reconstruction of Genome-Scale Metabolic Models becomes standard practice in systems biology, the number of organisms having at least one metabolic model at the genome-scale is peaking at an unprecedented scale. The automation of several laborious tasks, such as gap-finding and gap-filling, allowed to develop GSMMs for poorly described organisms. However, such models quality can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. The Biological networks constraint-based In Silico Optimization (BioISO) is a computational tool aimed at accelerating the reconstruction of Genome-Scale Metabolic Models. This tool facilitates the 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 to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased using GSMMs available in literature and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). Furthermore, BioISO was used as Menecos gap-finding algorithm to reduce the number of proposed solutions (reaction sets) for filling the gaps. BioISO was implemented as a webserver available at https://bioiso.bio.di.uminho.pt; and integrated into merlin as a plugin. BioISOs implementation as a Python package can also be retrieved from https://github.com/BioSystemsUM/BioISO.

systems biology↗

bioRxiv2021

merlin v4.0: an updated platform 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 users 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 non-template based draft reconstructions, while achieving competitive performance compared to state-of-the art tools both for well and less-studied organisms.

bioinformatics↗

bioRxiv2020

MORPHO-MOLECULAR MECHANISMS STUDY OF PHOTODYNAMIC THERAPY AND CURCUMIN LARVICIDE ACTION ON WILD MOSQUITOES LARVAE OF GENUS Aedes

IntroductionUntil the first two weeks of October 2019, 1.489,457 probable dengue cases have been reported in Brazil, with an incidence rate of 708.8 cases per 100 thousand inhabitants. Still in 2019, in the same period, 123.407 probable cases of chikungunya were reported, with 15 deaths confirmed by clinical and epidemiological criteria. Regarding Zika, in that period, 10.441 probable cases were recorded, been the northeast region with the highest number of notifications, followed by the midwest one. It is well known that current policies to control the vectors of those arboviruses are not effective. Studies for use of light-activated photosensitizers as an alternative to conventional insecticides for sustainable control of mosquitoes vector such as Aedes (dengue, yellow fever, chikungunya, zika), Anopheles (malaria), Culex (yellow fever) can already be found showing advantages over conventional insecticides (efficacy, safety, non-mutagenicity and fast degradation). ObjectiveTo evaluate the effectiveness of Photodynamic Therapy (PDT), mediated by curcumin and blue LED (460nm) in mortality of wild mosquitoes larvae of genus Aedes and also to verify, through confocal microscopy, how the photosensitizer internalizes in larvae organism. In addition to evaluating the action of PDT on the larvae with Raman spectroscopy and histological technique. Materials and methodsOvitraps were placed in the city of Vitoria da Conquista, Bahia, and the larvae collected in stages L2 and L3 were fed for 24 hours with curcumin in concentrations of 10, 20 and 50% mixed with fish feed and then subjected to irradiation with blue LED for 2h (22mW / cm2 and 158.4 J / cm2). The larvae were placed in a container with dechlorinated water and mortality was monitored for 24 and 48 hours. Control groups in which only the larvae were exposed to blue LED for 2 hours and in which the larvae were only fed with fish feed mixed with curcumin (10, 20 and 50%) were included in the study, in addition to the group without treatment. All experiments were repeated after a 2-month interval to confirm the results, totaling 240 tests (tests 1 and 2, n = 15) between groups PDT 10%, PDT 20%, PDT 50%, curcumin 10%, curcumin 20%, curcumin 50%, blue LED and untreated group. The larvae belonging to the PDT 20% group, 20% curcumin, blue LED and control were submitted to histological slides, confocal microscopy and Raman spectroscopy. Larvae mortality rates were compared between groups using univariate descriptive analysis. ResultsAll PDT groups showed larvicidal activity, with the PDT group 20% showing the highest larval mortality in the shortest time. The images from confocal microscopy by laser scanning showed that curcumin was distributed throughout the digestive system of larvae and the analysis by Raman microspectroscopy have shown patterns of alteration and cell death, corroborated by histological sections. ConclusionIt was concluded that PDT in all concentrations was effective in larval mortality, with PDT 20% having the best activity with mortality of 100% in 24 hours.

bioengineering↗

bioRxiv2019

Chlamydia trachomatis outbreak: when the virulence-associated genome backbone imports a prevalence-associated major antigen signature

Chlamydia trachomatis is the most prevalent sexually transmitted bacteria worldwide and the causative agent of blinding trachoma. Strains are classified based on ompA genotypes, which are strongly linked with differential tissue tropism and disease outcomes. A lymphogranuloma venereum (LGV) epidemics, characterized by ulcerative proctitis, has emerged in the last two decades (mainly L2b genotype), raising high concern especially due to its circulation among men who have sex with men (MSM). Here, we report an ongoing outbreak (mostly affecting HIV-positive MSM engaging in high-risk practices) caused by an L2b strain with a rather unique genome makeup that precluded the laboratory notification of this outbreak as LGV due to its non-LGV ompA signature. Homologous recombination mediated the transfer of a ~4.5Kbp fragment enrolling CT681/ompA and neighboring genes (CT677/rrf, CT678/pyrH, CT679/tsf, CT680/rpsB) from a serovar D/Da strain likely possessing the typical T1 clade genome backbone associated with most prevalent genotypes (E and F). The hybrid L2b/D-Da strain presents the adhesin and immunodominant antigen MOMP (coded by ompA) with an epitope repertoire typical of non-invasive genital strains, while keeping the genome-dispersed virulence fingerprint of a classical LGV (L2b) strain. As previously reported for inter-clade ompA exchange among non-LGV clades, this unprecedented C. trachomatis genomic mosaic involving a contemporary epidemiologically and clinically relevant LGV strain may have implications on its transmission, tissue tropism and pathogenic capabilities. The emergence of such variants with epidemic and pathogenic potential highlights the need of more oriented surveillance strategies focused on capturing the C. trachomatis evolution in action.

genomics↗