Biology subjects

Santos, A. R. d.

Publications and source records attributed to Santos, A. R. d..

6 recordsLinked to original sources

bioRxiv2025

Antifungal susceptibility profile of clinically relevant species of the genus Sporothrix: establishment of Epidemiological Cutoff Values (ECVs) according to CLSI broth microdilution methodology

Sporotrichosis is a globally distributed subcutaneous mycosis caused mainly by Sporothrix brasiliensis, S. schenckii, and S. globosa. Cat-transmitted sporotrichosis, primarily caused by S. brasiliensis in South America and to a lesser extent by S. schenckii in Southeast Asia, is emerging as a significant public health concern, due its outbreak potential. Itraconazole is the first-choice drug for treatment of human and cats, but reduced susceptibility has been reported based on previously proposed epidemiological cut-off values (ECVs). To support resistance surveillance, we aimed to establish CLSI-endorsed ECVs for these clinically relevant Sporothrix species. A total of 3,588 minimum inhibitory concentration (MIC) values for seven antifungal agents (amphotericin B, itraconazole, posaconazole, voriconazole, isavuconazole, olorofim, and terbinafine) were obtained from 19 international laboratories. Four of seven antifungals met the CLSI M57 guidelines criteria to determine the ECV. Established ECVs for amphotericin B were found to be high with 8 {micro}g/mL for S. brasiliensis and S. globosa, and 4 {micro}g/mL for S. schenckii. Itraconazole ECVs were 4 {micro}g/mL for S. brasiliensis and S. schenckii. Posaconazole ECVs were 4 {micro}g/mL for all three species (tentative for S. globosa), while the terbinafine ECV for S. brasiliensis was 0.12 {micro}g/mL. Olorofim demonstrated good in vitro activity, particularly against S. brasiliensis. Overall, this study establishes validated ECVs for key antifungals against Sporothrix species and identifies a low prevalence of non-wild type (NWT) isolates, supporting ongoing antifungal resistance monitoring.

microbiology↗

bioRxiv2024

The GenPPI tool enhanced Protein Interaction Network Generation with Machine Learning-Based Protein Similarity Inference

AbstractO_ST_ABSBackgroundC_ST_ABSComputational prediction of protein-protein interactions (PPIs) is crucial for understanding cell biology and drug development, offering an alternative to costly experimental methods. The original GenPPi software advanced ab initio PPI network prediction from bacterial genomes but was limited by its reliance on high sequence similarity. This work introduces GenPPi 1.5 to enhance these predictive capabilities. ResultsGenPPi 1.5 incorporates a Random Forest (RF) algorithm, trained on 60 biophysical features from amino acid propensity indices, to classify protein similarity even in low sequence identity scenarios (targeting >65% identity). To manage computational complexity from the increased interactions generated by the RF model, especially in extensive conserved phylogenetic profiles, we developed and integrated the Reduced Interaction Sampling (RIS) algorithm. RIS stochastically samples interactions within these profiles, optimizing performance for complete genome analysis. Extensive simulations across various configurations validated the methodology. RF integration significantly broadened GenPPis predictive power; application to Buchnera aphidicola showed up to 62% overlap with STRING database interactions. Analysis of RIS demonstrated that while introducing some randomness, critical node identification remains robust, particularly for Top N values[≥] 100, indicating minimal compromise to network integrity. ConclusionThe combination of Machine Learning (RF) and the RIS algorithm in GenPPi 1.5 represents a significant advancement. It overcomes the highsimilarity dependency of the previous version while efficiently handling complex genomes. GenPPi 1.5 provides a robust and scalable alignment-free PPI prediction solution, enabling users to train custom models tailored to specific genomic contexts. GenPPi is freely available on our website https://genppi.facom.ufu.br/, its source code is hosted on GitHub https://github.com/santosardr/genppi, and it can be easily installed via the Python Package Index using the command pip install genppipy.

bioinformatics↗

bioRxiv2024

Pannotator integrated with Medpipe provides immunological and subcellular location features using a microservice

Genome sequencing and assembly are trivial tasks nowadays. After assembling contigs and scaffolds from a genome, the subsequent step is annotation. An annotation evidencing the expected features, like rRNA, tRNA, and CDS, is a signal of the quality of our sequencing and assembly. Different techniques to obtain and reproduce DNA samples, as well as sequencing and assembly of genomes, can impact the quality of a genomes expected features. The Pannotator tool was conceived as an aid annotation tool focusing on the differences between assembling and its reference genome. Some of the key features for bacterial genome annotations are the subcellular location and immunological potential of a CDS. Instead of reimplementing the prediction of these features in Pannotator, we leveraged the capabilities of our microservice to provide them. In the end, Medpipe software was not modified, and Pannotator underwent minor changes to incorporate the subcellular location and immunological potential of all exported proteins annotated by the tool. Moreover, our Medpipe microservice can also be incorporated into other software. The Medpipe microservice is open to anyone, not only to our Pannotator tool. The successful integration of Medpipe to Pannotator, powered by the Medpipe microservice, offers a powerful approach to advanced genomic analysis. The Medpipe microservice, built on Kotlin with the Spring Boot framework, is instrumental in the automation of Medpipe processing. It achieves this using REST endpoints, such as the execution of Medpipe in an asynchronous manner, status retrieval, and prediction generation, which enhance the modularity and scalability of the microservice. The availability of endpoint documentation, detailed request examples, and logs make our microservice user-friendly. The results of this integration demonstrate the value of the information provided by Medpipe, enriching genomic annotation with additional details, such as the density of mature epitopes (MED) and protein subcellular location classification. The Pannotator has evolved beyond basic function annotation and now provides data on immunological potential, structure, and subcellular location after being integrated with our microservice. The Medpipe microservice is available at https://github.com/santosardr/medpipe-ms.git.

bioinformatics↗

bioRxiv2023

Crafting negative training datasets for a high-performance classification of bacterial proteins secreted via non-classical pathways

Understanding protein secretion pathways is paramount in studying diseases caused by bacteria and their respective treatments. Most such paths must signal ways to identify secretion. However, some proteins, known as non-classical secreted proteins, do not have signaling ways. This study aims to classify such proteins from predictive machine-learning techniques. We collected a set of physical-chemical characteristics of amino acids from the AA index site, bolding known protein motifs, like hydrophobicity. We developed a six-step method (Alignment, Preliminary classification, mean outliers, two Clustering algorithms, and Random choice) to filter data from raw genomes and compose a negative dataset in contrast to a positive dataset of 141 proteins from the literature. Using a conventional Random Forest machine-learning algorithm, we obtained an accuracy of 91% on classifying non-classical secreted proteins in a validation dataset with 14 positive and 92 negative proteins - sensitivity and specificity of 91 and 86%, respectively, performance compared to state of the art for non-classical secretion classification. However, this works novelty resides in the fastness of executing non-CSP classification: instead of dozens of seconds to just one second considering a few dozen protein samples or only ten seconds to classify one hundred thousand proteins. Such fastness is more suitable for pan-genomic analyses than current methods without losing accuracy. Therefore, this research has shown that selecting an appropriate descriptors set and an expressive training dataset compensates for not using an advanced machine learning algorithm for the secretion by non-classical pathways purpose. Available at https://github.com/santosardr/non-CSPs.

bioinformatics↗

bioRxiv2022

Is the production of reactive oxygen and nitrogen species by macrophages associated with better infectious control in the experimental disseminated and pulmonary mucormycosis?

Different levels of resistance against Rhizopus oryzae infection have been observed between inbred (BALB/c) and outbred (Swiss) mice, with is associated with the genetic background of each mouse strain. Considering that macrophages play an important role in host resistance to Rhizopus species, we use the different infectious outcomes observed in experimental mucormycosis to identify the most efficient macrophages responses pattern against R. oryzae in vitro and in vivo. For this, we compared BALB/c and Swiss macrophage activity pre-and-post intravenous or intratracheal R. oryzae infections. Production of hydrogen peroxide (H2O2) and nitric oxide (NO) was determined in cultures of peritoneal (PM{Phi}) or alveolar macrophages (AM{Phi}) challenged, or not, with heat-killed spores of R. oryzae. Levels of TNF- and IL-10 were also measured to enhance our findings. Naive PM{Phi} from BALB/c increased the production of H2O2, TNF-, and IL-10 in the presence of heat-killed spores of R. oryzae, while naive PM{Phi} from Swiss mice was less responsive. Naive AM{Phi} from two strains of mice were less reactive to heat-killed spores of R. oryzae than PM{Phi}. On 30 days of R. oryzae intravenous infection, lower fungal load in BALB/c strain of mice was accompanied by higher production of H2O2 by PM{Phi} when compared with Swiss mice. Differently, AM{Phi} from BALB/c mice showed higher production of NO, TNF-, and IL-10 after 7 days of intratracheal infection and after 30 days, lower fungal load, when compared with Swiss mice. According to the set of experiments performed, our findings reveal that independently of mice strain, PM{Phi} is more reactive against R. oryzae in the first contact than AM{Phi}. In addition, increased PM{Phi} production of H2O2 at the end of disseminated infection is related to efficient fungal clearance observed in resistant (BALB/c). Our findings provide new evidence to understand the parasite-hosts relationship in mucormycosis.

immunology↗

bioRxiv2021

GENPPI: standalone software for creating protein interaction networks from genomes

MotivationBacterial genomes are being deposited into online databases at an increasing rate. Genome annotation represents one of the first efforts to understand organisms and their diseases. Some evolutionary relationships that are capable of being annotated only from genomes are conserved gene neighbourhoods (CNs), phylogenetic profiles (PPs), and gene fusions. At present, there is no standalone software that enables networks of interactions among proteins to be created using these three evolutionary characteristics with efficient and effective results. ResultsWe developed GENPPI software for the ab initio prediction of interaction networks using predicted proteins from a genome. In our case study, we employed 50 genomes of the genus Corynebacterium. Based on the PP relationship, GENPPI differentiated genomes between the ovis and equi biovars of the species Corynebacterium pseudotuberculosis and created groups among the other species analysed. If we inspected only the CN relationship, we could not entirely separate biovars, only species. Our software GENPPI was determined to be efficient because, for example, it creates interaction networks from the central genomes of 50 species/lineages with an average size of 2200 genes in less than 40 minutes on a conventional computer. Our software is compelling because the interaction networks that it creates reflect evolutionary relationships among species and were obtained in average nucleotide identity (ANI) analyses. Additionally, this software enables the user to define how he or she intends to explore the PP and CN characteristics through various parameters, enabling the creation of customized interaction networks. For instance, users can set parameters regarding the genus, metagenome, or pangenome. In addition to the parameterization of GENPPI, it is also the users choice regarding which set of genomes he or she is going to study. AvailabilityThe source code in the Common Lisp language, binary files for different operating systems, and GENPPI software tutorials are available at {{github.com/santosardr/genppi}}. Contactsantosardr@ufu.br Supplementary informationSupplementary data are available at Bioinformatics online.

systems biology↗