In recent years, peptides have gained significant relevance due to their therapeutic properties. The surge in peptide production and synthesis has generated vast amounts of data, enabling the creation of comprehensive databases and information repositories. Advances in sequencing techniques and artificial intelligence have further accelerated the design of tailor-made peptides. However, leveraging these techniques requires versatile and continuously updated storage systems, along with tools that facilitate peptide research and the implementation of machine learning for predictive systems. This work introduces Peptipedia v2.0, one of the most comprehensive public repositories of peptides, supporting biotechnological research by simplifying peptide study and annotation. Peptipedia v2.0 has expanded its collection by over 45% with peptide sequences that have reported biological activities. The functional biological activity tree has been revised and enhanced, incorporating new categories such as cosmetic and dermatological activities, molecular binding, and antiageing properties. Utilizing protein language models and machine learning, more than 90 binary classification models have been trained, validated, and incorporated into Peptipedia v2.0. These models exhibit average sensitivities and specificities of 0.877±0.0530 and 0.873±0.054, respectively, facilitating the annotation of more than 3.6 million peptide sequences with unknown biological activities, also registered in Peptipedia v2.0. Additionally, Peptipedia v2.0 introduces description tools based on structural and ontological properties and user-friendly machine learning tools to facilitate the application of machine learning strategies to study peptide sequences. Database URL: https://peptipedia.cl/

Clinical and molecular heterogeneity are hallmarks of chronic lymphocytic leukemia (CLL), a neoplasm characterized by accumulation of mature and clonal long-lived CD5 + B-lymphocytes. Mutational status of the IgHV gene of leukemic clones is a powerful prognostic tool in CLL, and it is well established that unmutated CLLs (U-CLLs) have worse evolution than mutated cases. Nevertheless, progression and treatment requirement of patients can evolve independently from the mutational status. Microenvironment signaling or epigenetic changes partially explain this different behavior. Thus, we think that detailed characterization of the miRNAs landscape from patients with different clinical evolution could facilitate the understanding of this heterogeneity. Since miRNAs are key players in leukemia pathogenesis and evolution, we aim to better characterize different CLL behaviors by comparing the miRNome of clinically progressive U-CLLs vs. stable U-CLLs. Our data show up-regulation of miR-26b-5p, miR-106b-5p, and miR-142-5p in progressive cases and indicate a key role for miR-26b-5p during CLL progression. Specifically, up-regulation of miR-26b-5p in CLL cells blocks TGF-β/SMAD pathway by down-modulation of SMAD-4, resulting in lower expression of p21^−Cip1 kinase inhibitor and higher expression of c-Myc oncogene. This work describes a new molecular mechanism linking CLL progression with TGF-β modulation and proposes an alternative strategy to explore in CLL therapy.

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.

In recent years, peptides have gained significant relevance due to their therapeutic properties. The surge in peptide production and synthesis has generated vast amounts of data, enabling the creation of comprehensive databases and information repositories. Advances in sequencing techniques and artificial intelligence have further accelerated the design of tailor-made peptides. However, leveraging these techniques requires versatile and continuously updated storage systems, along with tools that facilitate peptide research and the implementation of machine learning for predictive systems. This work introduces Peptipedia v2.0, one of the most comprehensive public repositories of peptides, supporting biotechnological research by simplifying peptide study and annotation. Peptipedia v2.0 has expanded its collection by over 45% with peptide sequences that have reported biological activities. The functional biological activity tree has been revised and enhanced, incorporating new categories such as cosmetic and dermatological activities, molecular binding, and anti-ageing properties. Utilizing protein language models and machine learning, more than 90 binary classification models have been trained, validated, and incorporated into Peptipedia v2.0. These models exhibit average sensitivities and specificities of 0.877 ± 0.0530 and 0.873 ±0.054, respectively, facilitating the annotation of more than 3.6 million peptide sequences with unknown biological activities, also registered in Peptipedia v2.0. Additionally, Peptipedia v2.0 introduces description tools based on structural and ontological properties and user-friendly machinelearning tools to facilitate the application of machine-learning strategies to study peptide sequences. Peptipedia v2.0 is accessible under the Creative Commons CC BY-NC-ND 4.0 license at https://peptipedia.cl/ .

Gestión local de la información en biodiversidad: fomentando la ciencia participativa en el sur de Chile. La caracterización, la cuantificación y el seguimiento de la biodiversidad han sido algunos de los principales retos de la conservación. El Sistema de Información en Biodiversidad para Aysén (SIB-Aysén) es una plataforma colaborativa e interactiva para la consulta y gestión de datos biológicos que actualmente contiene más de 10 000 registros de unas 2800 especies de plantas, animales, hongos y microalgas presentes en la región subantártica de Aysén, Chile. Para construir la base de datos, se recopilaron los registros publicados en revistas especializadas, publicaciones divulgativas y documentos técnicos validados por la comunidad científica y se sistematizaron de acuerdo al estándar Darwin Core. En la plataforma, construida usando herramientas de libre acceso, para cada especie se muestra una ficha con descripción general, galería de fotografías y registros geolocalizados. Se encuentra disponible para todo público en internet a través del enlace https://kataix.umag.cl/sib-aysen. Se dispone de un módulo para la participación ciudadana que permite a las comunidades locales ingresar sus propios registros participativos, los cuales deben ser validados por especialistas. El proceso participativo se ha desarrollado mediante diversas iniciativas de innovación social como Hongusto y el Laboratorio Abierto de Ciencias Subantárticas, en los que se han involucrado científicos y comunidades locales. En este artículo se presenta el proceso de generación de este sistema para ampliar el conocimiento y valoración de la biodiversidad local. Local management of biodiversity information: fostering community-based science in southern Chile. The Biodiversity Information Facility for Aysén (SIB-Aysén) is a collaborative and interactive platform for consultation and management of biological data that currently contains more than 10 000 records of some 2800 species of plants, animals, fungi and microalgae present in the sub-Antarctic region of Aysén, Chile. To build the database, records published in specialized journals, informative publications and technical documents validated by the scientific community were compiled and systematized according to the Darwin Core standard. The platform, built using open-access tools, shows for each species a file with a general description, photo gallery and georeferenced records. It is available to the public on the Internet through the link https://kataix.umag.cl/sib-aysen. There is a module for citizen participation that allows local communities to enter their own participatory records, which must be validated by specialists. The participatory process has been developed through various social innovation initiatives such as Hongusto and the Open Laboratory of Subantarctic Sciences, in which scientists and local communities have been involved. This article presents the process of generating this system to expand the knowledge and valuation of local biodiversity.