The Virome of two Fusarium spp. Collections and its Potential for Controlling Fusarium Disease

This research aims to discover the virome diversity and composition in Fusarium poae and Fusarium proliferatum collections, characterize the mycovirus that may have an effect on host pathogenicity to provide potential materials for the biological control of Fusarium spp. pathogens. Next-Generation Sequencing (NGS) analysis of 30 F. poae isolates revealed an extreme diversity of mycoviruses. Bioinformatic analysis shows that contigs associated with viral genome belong to the families: Hypoviridae, Mitoviridae, Partitiviridae, Polymycoviridae, proposed Alternaviridae, proposed Fusagraviridae, proposed Fusariviridae, proposed Yadokariviridae, and Totiviridae. The complete genomes of 12 viruses were obtained by assembling contigs and overlapping cloning sequences. Moreover, all the F. poae isolates analyzed are multi-infected. Fusarium poae partitivirus 1 appears in all the 30 strains, followed by Fusarium poae fusagravirus 1 (22), Fusarium poae mitovirus 2 (18), Fusarium poae partitivirus 3 (16), and Fusarium poae mitovirus 2 and 3 (11). Using the same approach, the virome of F. proliferatum collections resulted in lower diversity and abundance. The identified mycoviruses belong to the family Mitoviridae and Mymonaviridae. Interestingly, most F. proliferatum isolates are not multi-infected. The complete genomes of four viruses were obtained by assembling contigs and overlapping cloning sequences. By multiple liner regression of the virome composition and growth rate of 30 F. poae, Fusarium poae mitovirus 3 is significantly correlated with the growth rate among F. poae collection. Furthermore, the principal component analysis of the virome composition from 30 F. poae showed that the presence of Fusarium poae mitovirus 3 and other two viruses could increase the F. poae growth rate. The curing experiment and pathogenicity test in Petri indicated that Fusarium poae hypovirus 1 might be associated with the host hypovirulence phenotype, while Fusarium poae fusagravirus 1 and Fusarium poae partitivirus 3 may have some beneficial effect on host pathogenicity.

TiMMing: developing an innovative suite of bioinformatic tools to harmonize and track the origin of copy number alterations in the evolutive history of multiple myeloma

Multiple Myeloma (MM) is a hematologic cancer with heterogeneous and complex genomic landscape, where Copy Number Alterations (CNAs) play a key role in the disease's pathogenesis and prognosis. It is of biological and clinical interest to study the temporal occurrence of early alterations, as they play a disease "driver" function by deregulating key tumor pathways. This study presents an innovative bioinformatic tools suite created for harmonizing and tracing the origin of CNAs throughout the evolutionary history of MM. To this aim, large cohorts of newly-diagnosed MM (NDMM, N=1582) and Smoldering-MM (SMM, N=282) were aggregated. The tools developed in this study enable the harmonization of CNAs as obtained from different genomic platforms in such a way that a high statistical power can be obtained. By doing so, the high numerosity of those cohorts was harnessed for the identification of novel genes characterized as "driver" (NFKB2, NOTCH2, MAX, EVI5 and MYC-ME2-enhancer), and the generation of an innovative timing model, implemented with a statistical method to introduce confidence intervals in the CNAs-calls. By applying this model on both NDMM and SMM cohorts, it was possible to identify specific CNAs (1q(CKS1B)amp, 13q(RB1)del, 11q(CCND1)amp and 14q(MAX)del) and categorize them as "early"/ "driver" events. A high level of precision was guaranteed by the narrow confidence intervals in the timing estimates. These CNAs were proposed as critical MM alterations, which play a foundational role in the evolutionary history of both SMM and NDMM. Finally, a multivariate survival model was able to identify the independent genomic alterations with the greatest effect on patients’ survival, including RB1-del, CKS1B-amp, MYC-amp, NOTCH2-amp and TRAF3-del/mut. In conclusion, the alterations that were identified as both "early-drivers” and correlated with patients’ survival were proposed as biomarkers that, if included in wider survival models, could provide a better disease stratification and an improved prognosis definition.

Identification of outcome-oriented cut-offs for copy number alterations in multiple myeloma: predictive biomarkers with improved prognostic accuracy

Aim of the present study was to develop a statistical approach to define the best cut-off Copy number alterations (CNAs) calling from genomic data provided by high throughput experiments, able to predict a specific clinical end-point (early relapse, 18 months) in the context of Multiple Myeloma (MM). 743 newly diagnosed MM patients with SNPs array-derived genomic and clinical data were included in the study. CNAs were called both by a conventional (classic, CL) and an outcome-oriented (OO) method, and Progression Free Survival (PFS) hazard ratios of CNAs called by the two approaches were compared. The OO approach successfully identified patients at higher risk of relapse and the univariate survival analysis showed stronger prognostic effects for OO-defined high-risk alterations, as compared to that defined by CL approach, statistically significant for 12 CNAs. Overall, 155/743 patients relapsed within 18 months from the therapy start. A small number of OO-defined CNAs were significantly recurrent in early-relapsed patients (ER-CNAs) - amp1q, amp2p, del2p, del12p, del17p, del19p -. Two groups of patients were identified either carrying or not ≥1 ER-CNAs (249 vs. 494, respectively), the first one with significantly shorter PFS and overall survivals (OS) (PFS HR 2.15, p<0001; OS HR 2.37, p<0.0001). The risk of relapse defined by the presence of ≥1 ER-CNAs was independent from those conferred both by R-IIS 3 (HR=1.51; p=0.01) and by low quality (< stable disease) clinical response (HR=2.59 p=0.004). Notably, the type of induction therapy was not descriptive, suggesting that ER is strongly related to patients’ baseline genomic architecture. In conclusion, the OO- approach employed allowed to define CNAs-specific dynamic clonality cut-offs, improving the CNAs calls’ accuracy to identify MM patients with the highest probability to ER. As being outcome-dependent, the OO-approach is dynamic and might be adjusted according to the selected outcome variable of interest.