Marco Roncador

The multiple myeloma (MM) genome is heterogeneous and evolves through preclinical and post-diagnosis phases. Here we report a catalog and hierarchy of driver lesions using sequences from 67 MM genomes serially collected from 30 patients together with public exome datasets. Bayesian clustering defines at least 7 genomic subgroups with distinct sets of co-operating events. Focusing on whole genome sequencing data, complex structural events emerge as major drivers, including chromothripsis and a novel replication-based mechanism of templated insertions, which typically occur early. Hyperdiploidy also occurs early, with individual trisomies often acquired in different chronological windows during evolution, and with a preferred order of acquisition. Conversely, positively selected point mutations, whole genome duplication and chromoplexy events occur in later disease phases. Thus, initiating driver events, drawn from a limited repertoire of structural and numerical chromosomal changes, shape preferred trajectories of evolution that are biologically relevant but heterogeneous across patients.

Of the many types of DNA damage, DNA double-strand breaks (DSBs) are probably the most deleterious. Mounting evidence points to an intricate relationship between DSBs and transcription. A cell system in which the impact on transcription can be investigated at precisely mapped genomic DSBs is essential to study this relationship. Here in a human cell line, we map genome-wide and at high resolution the DSBs induced by a restriction enzyme, and we characterize their impact on gene expression by four independent approaches by monitoring steady-state RNA levels, rates of RNA synthesis, transcription initiation and RNA polymerase II elongation. We consistently observe transcriptional repression in proximity to DSBs. Downregulation of transcription depends on ATM kinase activity and on the distance from the DSB. Our study couples for the first time, to the best of our knowledge, high-resolution mapping of DSBs with multilayered transcriptomics to dissect the events shaping gene expression after DSB induction at multiple endogenous sites.

Microbial communities living in nine vineyards distributed over three altitudinal transects were studied over 2 years. Fungal and bacterial community dynamics were explored using automated ribosomal intergenic spacer analysis (ARISA) and by determining bacterial cells and fungal colony-forming units (CFUs). Moreover, extensive chemical and physical analyses of the soils were carried out. Multivariate analyses demonstrated that bacterial and fungal communities are affected by altitude, which acts as a complex physicochemical gradient. In fact, soil moisture, Al, Mg, Mn and clay content are changing with altitude and influencing the bacterial genetic structure, while in the case of fungi, soil moisture, B and clay content are found to be the main drivers of the community. Moreover, other exchangeable cations and heavy metals, not correlating with altitude, are involved in the ordination of the sites, especially Cu. Qualitative ARISA revealed the presence of a stable core microbiome of operational taxonomic units (OTUs) within each transect, which ranged between 57% and 68% of total OTUs in the case of fungi and between 63% and 72% for bacteria. No seasonal effect on the composition of microbial communities was found, demonstrating that bacterial and fungal communities in vineyards are mostly stable over the considered seasons.

Abstract Multiple myeloma (MM) has a heterogeneous genome, evolving through both pre-clinical and post-diagnosis phases. Here, using sequences from 67 MM genomes serially collected from 30 patients together with public datasets, we establish a hierarchy of driver lesions. Point mutations, structural variants and copy number aberrations define at least 7 genomic subgroups of MM, each with distinct sets of co-operating driver mutations. Complex structural events are major drivers of MM, including chromothripsis, chromoplexy and a replication-based mechanism of templated insertions: these typically occur early. Hyperdiploidy also occurs early, with individual chromosomes often gained in more than one chronological epoch of MM evolution, showing a preferred order of acquisition. Positively selected point mutations frequently occur in later phases of disease development, as do structural variants involving MYC . Thus, initiating driver events of MM, drawn from a limited repertoire of structural and numerical chromosomal changes, shape preferred trajectories of subsequent evolution.

High-dose melphalan (HDM) improves progression-free survival in multiple myeloma (MM), yet melphalan is a DNA-damaging alkylating agent; therefore, we assessed its mutational effect on surviving myeloma cells by analyzing paired MM samples collected at diagnosis and relapse in the IFM 2009 study. We performed deep whole-genome sequencing on samples from 68 patients, 43 of whom were treated with RVD (lenalidomide, bortezomib, and dexamethasone) and 25 with RVD + HDM. Although the number of mutations was similar at diagnosis in both groups (7137 vs 7230; P = .67), the HDM group had significantly more mutations at relapse (9242 vs 13 383, P = .005). No change in the frequency of copy number alterations or structural variants was observed. The newly acquired mutations were typically associated with DNA damage and double-stranded breaks and were predominantly on the transcribed strand. A machine learning model, using this unique pattern, predicted patients who would receive HDM with high sensitivity, specificity, and positive prediction value. Clonal evolution analysis showed that all patients treated with HDM had clonal selection, whereas a static progression was observed with RVD. A significantly higher percentage of mutations were subclonal in the HDM cohort. Intriguingly, patients treated with HDM who achieved complete remission (CR) had significantly more mutations at relapse yet had similar survival rates as those treated with RVD who achieved CR. This similarity could have been due to HDM relapse samples having significantly more neoantigens. Overall, our study identifies increased genomic changes associated with HDM and provides rationale to further understand clonal complexity.