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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Metagenome Assembly and Metagenome-Assembled Genome of "Candidatus Aramenus sulfurataquae" from Thermal Sediments from the Los Azufres Volcanic Complex.

A plethora of hot springs are found at the Los Azufres volcanic complex in Mexico, and studies are needed to determine their microbial genomic diversity. Here, we report a metagenome of hot spring sediments and a metagenome-assembled genome of "Candidatus Aramenus sulfurataquae." This study reveals novel genomic sequences of Sulfolobales archaea.

Mutation in a SARS-CoV-2 Haplotype from Sub-Antarctic Chile Reveals New Insights into the Spike's Dynamics.

The emergence of SARS-CoV-2 variants, as observed with the D614G spike protein mutant and, more recently, with B.1.1.7 (501Y.V1), B.1.351 (501Y.V2) and B.1.1.28.1 (P.1) lineages, represent a continuous threat and might lead to strains of higher infectivity and/or virulence. We report on the occurrence of a SARS-CoV-2 haplotype with nine mutations including D614G/T307I double-mutation of the spike. This variant expanded and completely replaced previous lineages within a short period in the subantarctic Magallanes Region, southern Chile. The rapid lineage shift was accompanied by a significant increase of cases, resulting in one of the highest incidence rates worldwide. Comparative coarse-grained molecular dynamic simulations indicated that T307I and D614G belong to a previously unrecognized dynamic domain, interfering with the mobility of the receptor binding domain of the spike. The T307I mutation showed a synergistic effect with the D614G. Continuous surveillance of new mutations and molecular analyses of such variations are important tools to understand the molecular mechanisms defining infectivity and virulence of current and future SARS-CoV-2 strains.

The RUNX1b Isoform Defines Hemogenic Competency in Developing Human Endothelial Cells.

The transcription factor RUNX1 is a master regulator of blood cell specification. During embryogenesis, hematopoietic progenitors are initially generated from hemogenic endothelium through an endothelium-to-hematopoietic transition controlled by RUNX1. Several studies have dissected the expression pattern and role of RUNX1 isoforms at the onset of mouse hematopoiesis, however the precise pattern of RUNX1 isoform expression and biological output of RUNX1-expressing cells at the onset of human hematopoiesis is still not fully understood. Here, we investigated these questions using a RUNX1b:VENUS RUNX1c:TOMATO human embryonic stem cell line which allows multi-parameter single cell resolution via flow cytometry and isolation of RUNX1b-expressing cells for further analysis. Our data reveal the sequential expression of the two RUNX1 isoforms with RUNX1b expressed first in a subset of endothelial cells and during the endothelial to hematopoietic transition while RUNX1c only becomes expressed in fully specified blood cells. Furthermore, our data show that RUNX1b marks endothelial cells endowed with hemogenic potential and that RUNX1b expression level determines hemogenic competency in a dose-dependent manner. Together our data reveal the dynamic of RUNX1 isoforms expression at the onset of human blood specification and establish RUNX1b isoform as the earliest known marker for hemogenic competency.

EVI1 phosphorylation at S436 regulates interactions with CtBP1 and DNMT3A and promotes self-renewal.

The transcriptional regulator EVI1 has an essential role in early development and haematopoiesis. However, acute myeloid leukaemia (AML) driven by aberrantly high EVI1 expression has very poor prognosis. To investigate the effects of post-translational modifications on EVI1 function, we carried out a mass spectrometry (MS) analysis of EVI1 in AML and detected dynamic phosphorylation at serine 436 (S436). Wild-type EVI1 (EVI1-WT) with S436 available for phosphorylation, but not non-phosphorylatable EVI1-S436A, conferred haematopoietic progenitor cell self-renewal and was associated with significantly higher organised transcriptional patterns. In silico modelling of EVI1-S436 phosphorylation showed reduced affinity to CtBP1, and CtBP1 showed reduced interaction with EVI1-WT compared with EVI1-S436A. The motif harbouring S436 is a target of CDK2 and CDK3 kinases, which interacted with EVI1-WT. The methyltransferase DNMT3A bound preferentially to EVI1-WT compared with EVI1-S436A, and a hypomethylated cell population associated by EVI1-WT expression in murine haematopoietic progenitors is not maintained with EVI1-S436A. These data point to EVI1-S436 phosphorylation directing functional protein interactions for haematopoietic self-renewal. Targeting EVI1-S436 phosphorylation may be of therapeutic benefit when treating EVI1-driven leukaemia.

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.