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.

Exposure to nanoplastics and nanomaterials either single and combined affects the gill-associated microbiome of the Antarctic soft-shelled clam Laternula elliptica.

Nanoplastics and engineering nanomaterials (ENMs) are contaminants of emerging concern (CECs), increasingly being detected in the marine environment and recognized as a potential threat for marine biota at the global level including in polar areas. Few studies have assessed the impact of these anthropogenic nanoparticles in the microbiome of marine invertebrates, however combined exposure resembling natural scenarios has been overlooked. The present study aimed to evaluate the single and combined effects of polystyrene nanoparticles (PS NP) as proxy for nanoplastics and nanoscale titanium dioxide (nano-TiO2) on the prokaryotic communities associated with the gill tissue of the Antarctic soft-shell clam Laternula elliptica, a keystone species of marine benthos Wild-caught specimens were exposed to two environmentally relevant concentrations of carboxylated PS NP (PS-COOH NP, ∼62 nm size) and nano-TiO2 (Aeroxide P25, ∼25 nm) as 5 and 50 µg/L either single and combined for 96h in a semi-static condition.Our findings show a shift in microbiome composition in gills of soft-shell clams exposed to PS NP and nano-TiO2 either alone and in combination with a decrease in the relative abundance of OTU1 (Spirochaetaceae). In addition, an increase of gammaproteobacterial OTUs affiliated to MBAE14 and Methylophagaceae (involved in ammonia denitrification and associated with low-quality water), and the OTU Colwellia rossensis (previously recorded in polluted waters) was observed. Our results suggest that nanoplastics and nano-TiO2 alone and in combination induce alterations in microbiome composition by promoting the increase of negative taxa over beneficial ones in the gills of the Antarctic soft-shell clam. An increase of two low abundance OTUs in PS-COOH NPs exposed clams was also observed. A predicted gene function analysis revealed that sugar, lipid, protein and DNA metabolism were the main functions affected by either PS-COOH NP and nano-TiO2 exposure. The molecular functions involved in the altered affiliated OTUs are novel for nano-CEC exposures.

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.

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.