Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides.

With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

Autonomous B-cell receptor signaling and genetic aberrations in chronic lymphocytic leukemia-phenotype monoclonal B lymphocytosis in siblings of patients with chronic lymphocytic leukemia.

Clonal expansion of CD5-expressing B cells, commonly designated as monoclonal B lymphocytosis (MBL), is a precursor condition for chronic lymphocytic leukemia (CLL). The mechanisms driving subclinical MBL B-cell expansion and progression to CLL, occurring in approximately 1% of affected individuals, are unknown. An autonomously signaling B-cell receptor (BCR) is essential for the pathogenesis of CLL. The objectives of this study were functional characterization of the BCR of MBL in siblings of CLL patients and a comparison of genetic variants in MBL-CLL sibling pairs. Screening of peripheral blood by flow cytometry detected 0.2-480 clonal CLL-phenotype cells per microliter (median: 37/µL) in 34 of 191 (17.8%) siblings of CLL patients. Clonal BCR isolated from highly purified CLL-phenotype cells induced robust calcium mobilization in BCR-deficient murine pre-B cells in the absence of external antigen and without experimental crosslinking. This autonomous BCR signal was less intense than the signal originating from the CLL BCR of their CLL siblings. According to genotyping by single nucleotide polymorphism array, whole exome, and targeted panel sequencing, CLL risk alleles were found with high and similar prevalence in CLL patients and MBL siblings, respectively. Likewise, the prevalence of recurrent CLL-associated genetic variants was similar between CLL and matched MBL samples. However, copy number variations and small variants were frequently subclonal in MBL cells, suggesting their acquisition during subclinical clonal expansion. These findings support a stepwise model of CLL pathogenesis, in which autonomous BCR signaling leads to a non-malignant (oligo)clonal expansion of CD5+ B cells, followed by malignant progression to CLL after acquisition of pathogenic genetic variants.

A Metagenome from a Steam Vent in Los Azufres Geothermal Field Shows an Abundance of Thermoplasmatales archaea and Bacteria from the Phyla Actinomycetota and Pseudomonadota.

Los Azufres National Park is a geothermal field that has a wide number of thermal manifestations; nevertheless, the microbial communities in many of these environments remain unknown. In this study, a metagenome from a sediment sample from Los Azufres National Park was sequenced. In this metagenome, we found that the microbial diversity corresponds to bacteria (Actinomycetota, Pseudomonadota), archaea (Thermoplasmatales and Candidatus Micrarchaeota and Candidatus Parvarchaeota), eukarya (Cyanidiaceae), and viruses (Fussellovirus and Caudoviricetes). The functional annotation showed genes related to the carbon fixation pathway, sulfur metabolism, genes involved in heat and cold shock, and heavy-metal resistance. From the sediment, it was possible to recover two metagenome-assembled genomes from Ferrimicrobium and Cuniculiplasma. Our results showed that there are a large number of microorganisms in Los Azufres that deserve to be studied.

Integration of Mutational Signature Analysis with 3D Chromatin Data Unveils Differential AID-Related Mutagenesis in Indolent Lymphomas.

Activation-induced deaminase (AID) is required for somatic hypermutation in immunoglobulin genes, but also induces off-target mutations. Follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), the most frequent types of indolent B-cell tumors, are exposed to AID activity during lymphomagenesis. We designed a workflow integrating de novo mutational signatures extraction and fitting of COSMIC (Catalogue Of Somatic Mutations In Cancer) signatures, with tridimensional chromatin conformation data (Hi-C). We applied the workflow to exome sequencing data from lymphoma samples. In 33 FL and 30 CLL samples, 42% and 34% of the contextual mutations could be traced to a known AID motif. We demonstrate that both CLL and FL share mutational processes dominated by spontaneous deamination, failures in DNA repair, and AID activity. The processes had equiproportional distribution across active and nonactive chromatin compartments in CLL. In contrast, canonical AID activity and failures in DNA repair pathways in FL were significantly higher within the active chromatin compartment. Analysis of DNA repair genes revealed a higher prevalence of base excision repair gene mutations (p = 0.02) in FL than CLL. These data indicate that AID activity drives the genetic landscapes of FL and CLL. However, the final result of AID-induced mutagenesis differs between these lymphomas depending on chromatin compartmentalization and mutations in DNA repair pathways.

EVI1 oncoprotein expression and CtBP1-association oscillate through the cell cycle.

Aberrantly high expression of EVI1 in acute myeloid leukaemia (AML) is associated with poor prognosis. For targeted treatment of EVI1 overexpressing AML a more detailed understanding of aspects of spatiotemporal interaction dynamics of the EVI1 protein is important. EVI1 overexpressing SB1690CB AML cells were used for quantification and protein interaction studies of EVI1 and ΔEVI1. Cells were cell cycle-synchronised by mimosine and nocodazole treatment and expression of EVI1 and related proteins assessed by western blot, immunoprecipitation and immunofluorescence. EVI1 protein levels oscillate through the cell cycle, and EVI1 is degraded partly by the proteasome complex. Both EVI1 and ΔEVI1 interact with the co-repressor CtBP1 but dissociate from CtBP1 complexes during mitosis. Furthermore, a large fraction of EVI1, but not ΔEVI1 or CtBP1, resides in the nuclear matrix. In conclusion, EVI1- protein levels and EVI1-CtBP1 interaction dynamics vary though the cell cycle and differ between EVI1 and ΔEVI1. These data ad to the functional characterisation of the EVI1 protein in AML and will be important for the development of targeted therapeutic approaches for EVI1-driven AML.

EVI1 carboxy-terminal phosphorylation is ATM-mediated and sustains transcriptional modulation and self-renewal via enhanced CtBP1 association.

The transcriptional regulator EVI1 has an essential role in early hematopoiesis and development. However, aberrantly high expression of EVI1 has potent oncogenic properties and confers poor prognosis and chemo-resistance in leukemia and solid tumors. To investigate to what extent EVI1 function might be regulated by post-translational modifications we carried out mass spectrometry- and antibody-based analyses and uncovered an ATM-mediated double phosphorylation of EVI1 at the carboxy-terminal S858/S860 SQS motif. In the presence of genotoxic stress EVI1-WT (SQS), but not site mutated EVI1-AQA was able to maintain transcriptional patterns and transformation potency, while under standard conditions carboxy-terminal mutation had no effect. Maintenance of hematopoietic progenitor cell clonogenic potential was profoundly impaired with EVI1-AQA compared with EVI1-WT, in particular in the presence of genotoxic stress. Exploring mechanistic events underlying these observations, we showed that after genotoxic stress EVI1-WT, but not EVI1-AQA increased its level of association with its functionally essential interaction partner CtBP1, implying a role for ATM in regulating EVI1 protein interactions via phosphorylation. This aspect of EVI1 regulation is therapeutically relevant, as chemotherapy-induced genotoxicity might detrimentally sustain EVI1 function via stress response mediated phosphorylation, and ATM-inhibition might be of specific targeted benefit in EVI1-overexpressing malignancies.

Xenopus: An in vivo model for imaging the inflammatory response following injury and bacterial infection.

A major goal in regenerative medicine is to identify therapies to facilitate our body׳s innate abilities to repair and regenerate following injury, disease or aging. In the past decade it has become apparent that the innate immune system is able to affect the speed and quality of the regenerative response through mechanisms that are not entirely clear. For this reason there has been a resurgent interest in investigating the role of inflammation during tissue repair and regeneration. Remarkably, there have only been a handful of such studies using organisms with high regenerative capacity. Here we perform a study of the inflammatory response following injury in Xenopus larvae, which are able to achieve scarless wound healing and to regenerate appendages, as a preamble into understanding the role that inflammation plays during tissue repair and regeneration in this organism. We characterized the morphology and migratory behavior of granulocytes and macrophages following sterile and infected wounding regimes, using various transgenic lines that labeled different types of myeloid lineages, including granulocytes and macrophages. Using this approach we found that the inflammatory response following injury and infection in Xenopus larvae is very similar to that seen in humans, suggesting that this model provides an easily tractable and medically relevant system to investigate inflammation following injury and infection in vivo.

Human TRIB2 Oscillates during the Cell Cycle and Promotes Ubiquitination and Degradation of CDC25C.

Tribbles homolog 2 (TRIB2) is a member of the mammalian Tribbles family of serine/threonine pseudokinases (TRIB1-3). Studies of TRIB2 indicate that many of the molecular interactions between the single Drosophila Tribbles (Trbl) protein and interacting partners are evolutionary conserved. In this study, we examined the relationship between TRIB2 and cell division cycle 25 (CDC25) family of dual-specificity protein phosphatases (mammalian homologues of Drosophila String), which are key physiological cell cycle regulators. Using co-immunoprecipitation we demonstrate that TRIB2 interacts with CDC25B and CDC25C selectively. Forced overexpression of TRIB2 caused a marked decrease in total CDC25C protein levels. Following inhibition of the proteasome, CDC25C was stabilized in the nuclear compartment. This implicates TRIB2 as a regulator of nuclear CDC25C turnover. In complementary ubiquitination assays, we show that TRIB2-mediated degradation of CDC25C is associated with lysine-48-linked CDC25C polyubiquitination driven by the TRIB2 kinase-like domain. A cell cycle associated role for TRIB2 is further supported by the cell cycle regulated expression of TRIB2 protein levels. Our findings reveal mitotic CDC25C as a new target of TRIB2 that is degraded via the ubiquitin proteasome system. Inappropriate CDC25C regulation could mechanistically underlie TRIB2 mediated regulation of cellular proliferation in neoplastic cells.

pTransgenesis: a cross-species, modular transgenesis resource.

As studies aim increasingly to understand key, evolutionarily conserved properties of biological systems, the ability to move transgenesis experiments efficiently between organisms becomes essential. DNA constructions used in transgenesis usually contain four elements, including sequences that facilitate transgene genome integration, a selectable marker and promoter elements driving a coding gene. Linking these four elements in a DNA construction, however, can be a rate-limiting step in the design and creation of transgenic organisms. In order to expedite the construction process and to facilitate cross-species collaborations, we have incorporated the four common elements of transgenesis into a modular, recombination-based cloning system called pTransgenesis. Within this framework, we created a library of useful coding sequences, such as various fluorescent protein, Gal4, Cre-recombinase and dominant-negative receptor constructs, which are designed to be coupled to modular, species-compatible selectable markers, promoters and transgenesis facilitation sequences. Using pTransgenesis in Xenopus, we demonstrate Gal4-UAS binary expression, Cre-loxP-mediated fate-mapping and the establishment of novel, tissue-specific transgenic lines. Importantly, we show that the pTransgenesis resource is also compatible with transgenesis in Drosophila, zebrafish and mammalian cell models. Thus, the pTransgenesis resource fosters a cross-model standardization of commonly used transgenesis elements, streamlines DNA construct creation and facilitates collaboration between researchers working on different model organisms.

Improving Radiology Report Generation Quality and Diversity through Reinforcement Learning and Text Augmentation.

Deep learning is revolutionizing radiology report generation (RRG) with the adoption of vision encoder-decoder (VED) frameworks, which transform radiographs into detailed medical reports. Traditional methods, however, often generate reports of limited diversity and struggle with generalization. Our research introduces reinforcement learning and text augmentation to tackle these issues, significantly improving report quality and variability. By employing RadGraph as a reward metric and innovating in text augmentation, we surpass existing benchmarks like BLEU4, ROUGE-L, F1CheXbert, and RadGraph, setting new standards for report accuracy and diversity on MIMIC-CXR and Open-i datasets. Our VED model achieves F1-scores of 66.2 for CheXbert and 37.8 for RadGraph on the MIMIC-CXR dataset, and 54.7 and 45.6, respectively, on Open-i. These outcomes represent a significant breakthrough in the RRG field. The findings and implementation of the proposed approach, aimed at enhancing diagnostic precision and radiological interpretations in clinical settings, are publicly available on GitHub to encourage further advancements in the field.

The oncogenic fusion protein TAZ::CAMTA1 promotes genomic instability and senescence through hypertranscription.

TAZ::CAMTA1 is a fusion protein found in over 90% of Epithelioid Hemangioendothelioma (EHE), a rare vascular sarcoma with an unpredictable disease course. To date, how TAZ::CAMTA1 initiates tumour formation remains unexplained. To study the oncogenic mechanism leading to EHE initiation, we developed a model system whereby TAZ::CAMTA1 expression is induced by doxycycline in primary endothelial cells. Using this model, we establish that upon TAZ::CAMTA1 expression endothelial cells rapidly enter a hypertranscription state, triggering considerable DNA damage. As a result, TC-expressing cells become trapped in S phase. Additionally, TAZ::CAMTA1-expressing endothelial cells have impaired homologous recombination, as shown by reduced BRCA1 and RAD51 foci formation. Consequently, the DNA damage remains unrepaired and TAZ::CAMTA1-expressing cells enter senescence. Knockout of Cdkn2a, the most common secondary mutation found in EHE, allows senescence bypass and uncontrolled growth. Together, this provides a mechanistic explanation for the clinical course of EHE and offers novel insight into therapeutic options.

CD82 expression marks the endothelium to hematopoietic transition at the onset of blood specification in human.

During embryonic development, all blood progenitors are initially generated from endothelial cells that acquire a hemogenic potential. Blood progenitors emerge through an endothelial-to-hematopoietic transition regulated by the transcription factor RUNX1. To date, we still know very little about the molecular characteristics of hemogenic endothelium and the molecular changes underlying the transition from endothelium to hematopoiesis. Here, we analyzed at the single cell level a human embryonic stem cell-derived endothelial population containing hemogenic potential. RUNX1-expressing endothelial cells, which harbor enriched hemogenic potential, show very little molecular differences to their endothelial counterpart suggesting priming toward hemogenic potential rather than commitment. Additionally, we identify CD82 as a marker of the endothelium-to-hematopoietic transition. CD82 expression is rapidly upregulated in newly specified blood progenitors then rapidly downregulated as further differentiation occurs. Together our data suggest that endothelial cells are first primed toward hematopoietic fate, and then rapidly undergo the transition from endothelium to blood.

The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management.

The ongoing COVID-19 pandemic is arguably one of the most challenging health crises in modern times. The development of effective strategies to control the spread of SARS-CoV-2 were major goals for governments and policy makers. Mathematical modeling and machine learning emerged as potent tools to guide and optimize the different control measures. This review briefly summarizes the SARS-CoV-2 pandemic evolution during the first 3 years. It details the main public health challenges focusing on the contribution of mathematical modeling to design and guide government action plans and spread mitigation interventions of SARS-CoV-2. Next describes the application of machine learning methods in a series of study cases, including COVID-19 clinical diagnosis, the analysis of epidemiological variables, and drug discovery by protein engineering techniques. Lastly, it explores the use of machine learning tools for investigating long COVID, by identifying patterns and relationships of symptoms, predicting risk indicators, and enabling early evaluation of COVID-19 sequelae.

EVI1 protein interaction dynamics: Targetable for therapeutic intervention?

High expression of the transcriptional regulator EVI1 encoded at the MECOM locus at 3q26 is one of the most aggressive oncogenic drivers in acute myeloid leukemia (AML) and carries a very poor prognosis. How EVI1 confers leukemic transformation and chemotherapy resistance in AML is subject to important ongoing clinical and experimental studies. Recent discoveries have revealed critical details on genetic mechanisms of the activation of EVI1 overexpression and downstream events of aberrantly high EVI1 expression. Here we review and discuss aspects concerning the protein interactions of EVI1 and the related proteins MDS-EVI1 and ΔEVI1 from the perspective of their potential for therapeutic intervention.

Metagenome-Assembled Genome of Acidibrevibacterium fodinaquatile FLA01 from Fumarole Sediments from the Los Azufres Geothermal Field.

The Los Azufres geothermal field contains diverse microbial communities inhabiting thermal springs whose bacterial genomic diversity is being analyzed. Here, we describe a metagenome-assembled genome of Acidibrevibacterium fodinaquatile FLA01 obtained from fumarole sediment sequencing data. The genome contained genes for carbon fixation, osmotic shock, and heavy metal resistance.

Infantile fibrosarcoma with TPM3-NTRK1 fusion in a boy with Bloom syndrome.

Bloom syndrome (BS) is a genomic and chromosomal instability disorder with prodigious cancer predisposition caused by pathogenic variants in BLM. We report the clinical and genetic details of a boy who first presented with infantile fibrosarcoma (IFS) at the age of 6 months and subsequently was diagnosed with BS at the age of 9 years. Molecular analysis identified the pathogenic germline BLM sequence variants (c.1642C>T and c.2207_2212delinsTAGATTC). This is the first report of IFS related to BS, for which we show that both BLM alleles are maintained in the tumor and demonstrate a TPM3-NTKR1 fusion transcript in the IFS. Our communication emphasizes the importance of long-term follow up after treatment for pediatric neoplastic conditions, as clues to important genetic entities might manifest later, and the identification of a heritable tumor predisposition often leads to changes in patient surveillance and management.

A comparison of Covid-19 early detection between convolutional neural networks and radiologists.

BACKGROUND: The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. METHODS: The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. RESULTS: Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. CONCLUSION: The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.

Genome-wide analysis of gene expression during Xenopus tropicalis tadpole tail regeneration.

BACKGROUND: The molecular mechanisms governing vertebrate appendage regeneration remain poorly understood. Uncovering these mechanisms may lead to novel therapies aimed at alleviating human disfigurement and visible loss of function following injury. Here, we explore tadpole tail regeneration in Xenopus tropicalis, a diploid frog with a sequenced genome. RESULTS: We found that, like the traditionally used Xenopus laevis, the Xenopus tropicalis tadpole has the capacity to regenerate its tail following amputation, including its spinal cord, muscle, and major blood vessels. We examined gene expression using the Xenopus tropicalis Affymetrix genome array during three phases of regeneration, uncovering more than 1,000 genes that are significantly modulated during tail regeneration. Target validation, using RT-qPCR followed by gene ontology (GO) analysis, revealed a dynamic regulation of genes involved in the inflammatory response, intracellular metabolism, and energy regulation. Meta-analyses of the array data and validation by RT-qPCR and in situ hybridization uncovered a subset of genes upregulated during the early and intermediate phases of regeneration that are involved in the generation of NADP/H, suggesting that these pathways may be important for proper tail regeneration. CONCLUSIONS: The Xenopus tropicalis tadpole is a powerful model to elucidate the genetic mechanisms of vertebrate appendage regeneration. We have produced a novel and substantial microarray data set examining gene expression during vertebrate appendage regeneration.

Biochemical characterization of nuclear receptors for vitamin D3 and glucocorticoids in prostate stroma cell microenvironment.

The disruption of stromal cell signals in prostate tissue microenvironment influences the development of prostate cancer to androgen independence. 1α,25-Dihydroxyvitamin D(3) (1,25D(3)) and glucocorticoids, either alone or in combination, have been investigated as alternatives for the treatment of advanced prostate cancers that fails androgen therapies. The effects of glucocorticoids are mediated by the intracellular glucocorticoid receptor (GR). Similarly, the effect of 1,25D(3) is mediated by the 1,25D(3) nuclear receptor (VDR). In this study, fibroblasts from benign- (BAS) and carcinoma-associated stroma (CAS) were isolated from human prostates to characterize VDR and GR function as transcription factors in prostate stroma. The VDR-mediated transcriptional activity assessed using the CYP24-luciferase reporter was limited to 3-fold induction by 1,25D(3) in 9 out of 13 CAS (70%), as compared to >10-fold induction in the BAS clinical sample pair. Expression of His-tagged VDR (Ad-his-VDR) failed to recover the low transcriptional activity of the luciferase reporter in 7 out of 9 CAS. Interestingly, expression of Ad-his-VDR successfully recovered receptor-mediated induction in 2 out of the 9 CAS analyzed, suggesting that changes in the receptor protein itself was responsible for decreased response and resistance to 1,25D(3) action. Conversely, VDR-mediated transcriptional activity was more efficient in 4 out of 13 CAS (30%), as compared to the BAS sample pair. Consistent with the reduced response to 1,25D(3) observed in CAS, chromatin immunoprecipitation (ChIP) assays indicated decreased recruitment of coactivators SRC-1/CBP, without major changes in the recruitment of VDR to the CYP24 promoter. In addition, we observed that GR-mediated transcriptional activity was also altered in CAS, as compared to BAS. Disruption of coactivators SRC-1/CBP recruitment may promote hormone resistance in CaP, and highlights the relevance of molecular diagnosis and drug design in tumor cell microenvironment.