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Specialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015. Since its conception, MIBiG has been regularly updated to expand data coverage and remain up to date with innovations in natural product research. Here, we describe MIBiG version 4.0, an extensive update to the data repository and the underlying data standard. In a massive community annotation effort, 267 contributors performed 8304 edits, creating 557 new entries and modifying 590 existing entries, resulting in a new total of 3059 curated entries in MIBiG. Particular attention was paid to ensuring high data quality, with automated data validation using a newly developed custom submission portal prototype, paired with a novel peer-reviewing model. MIBiG 4.0 also takes steps towards a rolling release model and a broader involvement of the scientific community. MIBiG 4.0 is accessible online at https://mibig.secondarymetabolites.org/., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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 groundbreaking feature in computational chemistry, DeepMol establishes itself as the pioneering state-of-the-art tool in the field.Scientific contributionDeepMol aims to provide an integrated framework of AutoML for computational chemistry. DeepMol provides a more robust alternative to other tools with its integrated pipeline serialization, enabling seamless deployment using the fit, transform, and predict paradigms. It uniquely supports both conventional and deep learning models for regression, classification and multi-task, offering unmatched flexibility compared to other AutoML tools. DeepMol's predefined configurations and customizable objective functions make it accessible to users at all skill levels while enabling efficient and reproducible workflows. Benchmarking on diverse datasets demonstrated its ability to deliver optimized pipelines and superior performance across various molecular machine-learning tasks., Competing Interests: Declarations. Competing interests: The authors declare that they have no competing interests., (© 2024. The Author(s).)

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.

Thyroid cancer is a common malignancy of the endocrine system. Nodules are routinely evaluated for malignancy risk by fine needle aspiration biopsy (FNAB), and in cases such as follicular lesions, differential diagnosis between benign and malignant nodules is highly uncertain. Therefore, the discovery of new biomarkers for this disease could be helpful in improving diagnostic accuracy. Thyroid nodule biopsies were subjected to a precipitation step with both the insoluble and supernatant fractions subjected to proteome and peptidome profiling. Proteomic analysis identified annexin A1 as a potential biomarker of thyroid cancer malignancy, with its levels increased in malignant samples. Also upregulated were the acetylated peptides of annexin A1, revealed by the peptidome analysis of the supernatant fraction. In addition, supernatant peptidomic analysis revealed a number of acetylated histone peptides that were significantly elevated in the malignant group, suggesting higher gene transcription activity in malignant tissue. Two of these peptides were found to be robust malignancy predictors, with an area under the receiver operating a characteristic curve (ROC AUC) above 0.95. Thus, this combination of proteomics and peptidomics analyses improved the detection of malignant lesions and also provided new evidence linking thyroid cancer development to heightened transcription activity. This study demonstrates the importance of peptidomic profiling in complementing traditional proteomics approaches.

Differentiated thyroid cancer is the most common malignancy of the endocrine system. Although most thyroid nodules are benign, given the high incidence of thyroid nodules in the population, it is important to understand the differences between benign and malignant thyroid cancer and the molecular alterations associated with malignancy to improve detection and signal potential diagnostic, prognostic, and therapeutic targets. Proteomics analysis of benign and malignant human thyroid tissue largely revealed changes indicating modifications in RNA regulation, a common cancer characteristic. In addition, changes in the immune system and cell membrane/endocytic processes were also suggested to be involved. Annexin A1 was considered a potential malignancy biomarker and, similarly to other annexins, it was found to increase in the malignant group. Furthermore, a bioinformatics approach points to the transcription factor Sp1 as being potentially involved in most of the alterations seen in the malignant thyroid nodules.

Genome-scale metabolic models (GEMs) are essential tools for in silico phenotype prediction and strain optimisation. The most straightforward GEMs reconstruction approach uses published models as templates to generate the initial draft, requiring further curation. Such an approach is used by BiGG Integration Tool (BIT), available for merlin users. This tool uses models from BiGG Models database as templates for the draft models. Moreover, BIT allows the selection between different template combinations. The main objective of this study is to assess the draft models generated using this tool and compare them BIT, comparing these to CarveMe models, both of which use the BiGG database, and curated models. For this, three organisms were selected, namely Streptococcus thermophilus , Xylella fastidiosa and Mycobacterium tuberculosis. The models' variability was assessed using reactions and genes' metabolic functions. This study concluded that models generated with BIT for each organism were differentiated, despite sharing a significant portion of metabolic functions. Furthermore, the template seems to influence the content of the models, though to a lower extent. When comparing each draft with curated models, BIT had better performances than CarveMe in all metrics. Hence, BIT can be considered a fast and reliable alternative for draft reconstruction for bacteria models., (© 2022 the author(s), published by De Gruyter, Berlin/Boston.)

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., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Background: There is increasing evidence regarding the influence of the intestinal microbiota on the disease processes of various organs and systems. Dysbiosis, that is, alteration of the composition and function of the microbiota may constitute an important risk factor for the development of mental disorders, namely, schizophrenia., Objective: This works aims to review current evidence regarding the pathological mechanisms leading from dysbiosis to schizophrenia and in particular the deficit syndrome in schizophrenia., Methods: Scientific articles from PubMed, SCOPUS, EMBASE, and Web of Science Core Collection published between September 2017 and December 2020 were included in this review., Results: The commensal intestinal flora plays an important role in neurodevelopment. In the presence of dysbiosis, this maturation gets disturbed, resulting in the modification of brain structures and inflammatory responses at the intestinal, systemic, and Central Nervous System (CNS) levels. These disturbances may be linked to the development of symptoms of the disease. The microbiota exerts its influence on the CNS through several pathways, however, in this paper we focused on the membrane hypothesis and the inflammatory hypothesis. We explored the evidence concerning the use of probiotics, prebiotics, and fecal transplants., Conclusion: Although there is no consensus regarding the alterations that could constitute a risk factor for schizophrenia, some of the species appear to be more frequently altered, and their relationship with the host is dysregulated in patients at risk and with established schizophrenia, particularly in deficit schizophrenia., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

A case is presented of a 57-year-old man consulting for chronic diarrhea. Based on subsequent findings (thyroid nodule and metastases), the possibility of metastatic medullary thyroid carcinoma (MTC) was raised. Thyroidectomy allowed diagnosing a multicentric left lobe MTC. MTC is a rare cause of diarrhea, but should be considered, especially in the presence of signs or symptoms of alarm or nonresponse to empirical therapy., Competing Interests: Sponsorships or competing interests that may be relevant to content are disclosed at the end of this articleThe authors declare no conflicts of interest., (Copyright © 2018 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of PBJ-Associação Porto Biomedical/Porto Biomedical Society. All rights reserved.)

Amphetamine (AMPH) is a psychostimulant used worldwide by millions of patients in the clinical treatment of attention deficit hyperactivity disorder, narcolepsy or even obesity, and is also a drug of abuse. 4-Hydroxynorephedrine (4-OHNE) and 4-hydroxyamphetamine (4-OHAMPH) are two major metabolites known to persist in the brain longer than AMPH. The contribution of AMPH metabolites for its neurotoxicity is undetermined. We evaluated the toxicity of AMPH and its metabolites 4-OHNE and 4-OHAMPH, obtained by chemical synthesis, in human dopaminergic differentiated SH-SY5Y neurons. Cells were exposed to AMPH (concentration range 0-5mM) or 4-OHAMPH or 4-OHNE (concentration range 0-10mM) for 24 or 48h, and the viability was determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) and lactate dehydrogenase (LDH) leakage assays. Results showed that for both AMPH and the metabolites a concentration-dependent toxicity was observed. The toxic concentration 50% (TC 50 ) for AMPH and 4-OHNE following 24h exposure was circa 3.5mM and 8mM, respectively. For 4-OHAMPH the TC 50 was not reached in the tested concentration range. N-acetyl cysteine, cycloheximide, l-carnitine, and methylphenidate were able to reduce cell death induced by AMPH TC 50 . Acridine orange/ethidium bromide staining showed evident signs of late apoptotic cells and necrotic cells following 24h exposure to AMPH 3.50mM. The 4-OHAMPH metabolite at 8.00mM originated few late apoptotic cells, whereas 4-OHNE at 8.00mM resulted in late apoptotic cells and necrotic cells, in a scenario similar to AMPH. In conclusion, the AMPH metabolite 4-OHNE is more toxic than 4-OHAMPH, nonetheless both are less toxic than the parent compound in vitro. The most toxic metabolite 4-OHNE has longer permanence in the brain, rendering likely its contribution for AMPH neurotoxicity., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Amphetamine-type psychostimulants (ATS) are used worldwide by millions of patients for several psychiatric disorders. Amphetamine (AMPH) and "ecstasy" (3,4-methylenedioxymethamphetamine or MDMA) are common drugs of abuse. The impact of chronic ATS exposure to neurons and brain aging is still undisclosed. Current neuronal culture paradigms are designed to access acute ATS toxicity. We report for the first time a model of chronic exposure to AMPH and MDMA using long-term rat cortical cultures. In two paradigms, ATS were applied to neurons at day 1 in vitro (DIV) (0, 1, 10 and 100 μM of each drug) up to 28 days (200 μM was applied to cultures up to 14 DIV). Our reincubation protocol assured no decrease in the neuronal media's drug concentration. Chronic exposure of neurons to concentrations equal to or above 100 μM of ATS up to 28 DIV promoted significant mitochondrial dysfunction and elicited neuronal death, which was not prevented by glutamate receptor antagonists at 14 DIV. ATS failed to promote accelerated senescence as no increase in β-galactosidase activity at 21 DIV was found. In younger cultures (4 or 8 DIV), AMPH promoted mitochondrial dysfunction and neuronal death earlier than MDMA. Overall, AMPH proved more toxic and was the only drug that decreased intraneuronal glutathione levels. Meanwhile, caspase 3 activity increased for either drug at 200 μM in younger cultures at 8 DIV, but not at 14 DIV. At 8 DIV, ATS promoted a significant change in the percentage of neurons and astroglia present in culture, promoting a global decrease in the number of both cells. Importantly, concentrations equal to or below 10 μM of either drug did not promote neuronal death or oxidative stress. Our paradigm of neuronal cultures long-term exposure to low micromolar concentrations of ATS closely reproduces the in vivo scenario, being valuable to study the chronic impact of ATS., (Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.)

A 43-year-old female with antiphospholipid syndrome and Graves' disease developed a cutaneous leukocytoclastic vasculitis associated with antineutrophil cytoplasmic antibody (ANCA) against myeloperoxidase (MPO-ANCA) and proteinase-3 (PR3-ANCA), whilst treated with propylthiouracil (PTU). The skin lesions were progressively resolved after withdrawal of PTU and treatment with oral steroids. Patch testing with PTU at 1%, 5%, and 10% in petrolatum was positive at 48 h. Despite positive ANCA titers after 1 year of follow-up, the patient maintains complete clinical remission. PTU is a common antithyroid drug, which has been known to induce ANCA-positive vasculitis. Although most patients with this rare side effect have a good outcome, some fatal cases have been reported. Therefore, patients treated with PTU should be carefully followed and monitored, not only for their thyroid state but also for early detection of potential serious complications of this drug. Early diagnosis and prompt cessation of PTU therapy are essential to improve the outcome. Also key aspects of PTU-induced ANCA-positive vasculitis are reviewed.

3,4-Methylenedioxymethamphetamine (MDMA or "ecstasy"), is a widely abused, psychoactive recreational drug that is known to induce neurotoxic effects. Human and rat hepatic metabolism of MDMA involves N-demethylation to 3,4-methylenedioxyamphetamine (MDA), which is also a drug of abuse. MDMA and MDA are O-demethylenated to N-methyl-alpha-methyldopamine (N-Me-alpha-MeDA) and alpha-methyldopamine (alpha-MeDA), respectively, which are both catechols that can undergo oxidation to the corresponding ortho-quinones. Ortho-quinones may be conjugated with glutathione (GSH) to form glutathionyl adducts, which can be transported into the brain and metabolized to the correspondent N-acetylcysteine (NAC) adducts. In this study we evaluated the neurotoxicity of nine MDMA metabolites, obtained by synthesis: N-Me-alpha-MeDA, alpha-MeDA and their correspondent GSH and NAC adducts. The studies were conducted in rat cortical neuronal cultures, for a 6 h of exposure period, under normal (36.5 degrees C) and hyperthermic (40 degrees C) conditions. Our findings show that thioether MDMA metabolites are strong neurotoxins, significantly more than their correspondent parent catechols. On the other hand, N-Me-alpha-MeDA and alpha-MeDA are more neurotoxic than MDMA. GSH and NAC conjugates of N-Me-alpha-MeDA and alpha-MeDA induced a concentration dependent delayed neuronal death, accompanied by activation of caspase 3, which occurred earlier in hyperthermic conditions. Furthermore, thioether MDMA metabolites time-dependently increased the production of reactive species, concentration-dependently depleted intracellular GSH and increased protein bound quinones. Finally, thioether MDMA metabolites induced neuronal death and oxidative stress was prevented by NAC, an antioxidant and GSH precursor. This study provides new insights into the neurotoxicity mechanisms of thioether MDMA metabolites and highlights their importance in "ecstasy" neurotoxicity.

Studies on 3,4-methylenedioxymethamphetamine ("ecstasy")-induced neurotoxicity mainly focus on damage of serotonergic terminals. Less attention has been given to neuronal cell death produced by 3,4-methylenedioxymethamphetamine and other amphetamines in areas including the cortex, striatum and thalamus. In the present study we investigated 3,4-methylenedioxymethamphetamine-induced neurotoxicity in neuronal serum free cultures from rat cortex. Since 3,4-methylenedioxymethamphetamine intake induces hyperthermia in both animals and humans, the experiments were performed under normal (36.5 degrees C) and hyperthermic conditions (40 degrees C). Our findings showed a dose-, time- and temperature-dependent apoptotic cell death induced by 3,4-methylenedioxymethamphetamine in cortical neurons. 3,4-Methylenedioxymethamphetamine-induced damage was potentiated under hyperthermia. The neurotoxicity was reduced by the serotonin 2A-receptor antagonists, ketanserin and (2R,4R)-5-[2-[2-[2-(3-methoxyphenyl)ethyl]phenoxy]ethyl]-1-methyl-3-pyrrolidinol hydrochloride, in both normothermic and hyperthermic conditions. (+/-)-2,5-Dimethoxy-4-iodoamphetamine hydrochloride, a model agonist for the serotonin 2A-receptor, also induced a dose- and time-dependent apoptotic cell death. Again, protection was provided by ketanserin and (2R,4R)-5-[2-[2-[2-(3-methoxyphenyl)ethyl]phenoxy]ethyl]-1-methyl-3-pyrrolidinol hydrochloride against (+/-)-2,5-dimethoxy-4-iodoamphetamine hydrochloride-induced neurotoxicity, thereby indicating that the 3,4-methylenedioxymethamphetamine stimulation of the serotonin 2A-receptor leads to neurotoxicity. This study provides for the first time evidence that direct 3,4-methylenedioxymethamphetamine serotonin 2A-receptor stimulation leads to neuronal cortical death. alpha-Phenyl-N-tert-butyl nitrone a free radical scavenger and the nitric oxide synthase inhibitor Nomega-nitro-L-arginine as well as the NMDA-receptor antagonist MK-801 provided protection under normothermia and hyperthermia, thereby suggesting the participation of free radicals in 3,4-methylenedioxymethamphetamine-induced cell death. Since 3,4-methylenedioxymethamphetamine serotonin 2A-receptor agonistic properties lead to neuronal death, clinically available atypical antipsychotic drugs with serotonin 2A-antagonistic properties could be a valuable therapeutic tool against 3,4-methylenedioxymethamphetamine-induced neurodegeneration.