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Jagannath, S.

Publications and source records attributed to Jagannath, S..

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Characterization and validation of 15 α-synuclein conformation-specific antibodies using well-characterized preparations of α-synuclein monomers, fibrils and oligomers with distinct structures and morphology: How specific are the conformation-specific α-synuclein antibodies?

Increasing evidence suggests that alpha-synuclein (-syn) oligomers are obligate intermediates in the pathway involved in -syn fibrillization and Lewy body (LB) formation, and may also accumulate within LBs in Parkinsons disease (PD) and other synucleinopathies. Therefore, the development of tools and methods to detect and quantify -syn oligomers has become increasingly crucial for mechanistic studies to understand the role of these oligomers in PD, and to develop new diagnostic methods and therapies for PD and other synucleinopathies. The majority of these tools and methods rely primarily on the use of aggregation state-specific or conformation-specific antibodies. Given the impact of the data and knowledge generated using these antibodies on shaping the foundation and directions of -syn and PD research, it is crucial that these antibodies are thoroughly characterized, and their specificity or ability to capture diverse -syn species is tested and validated. Herein, we describe an antibody characterization and validation pipeline that allows a systematic investigation of the specificity of -syn antibodies using well-defined and well-characterized preparations of various -syn species, including monomers, fibrils, and different oligomer preparations that are characterized by distinct morphological, chemical and secondary structure properties. This pipeline was used to characterize 18 -syn antibodies, 16 of which have been reported as conformation- or oligomer-specific antibodies, using an array of techniques, including immunoblot analysis (slot blot and Western blot), a digital ELISA assay using single molecule array technology and surface plasmon resonance. Our results show that i) none of the antibodies tested are specific for one particular type of -syn species, including monomers, oligomers or fibrils; ii) all antibodies that were reported to be oligomer-specific also recognized fibrillar -syn; and iii) a few antibodies showed high specificity for oligomers and fibrils but did not bind to monomers. These findings suggest that the great majority of -syn aggregate-specific antibodies do not differentiate between oligomers and fibrils, thus highlighting the importance of exercising caution when interpreting results obtained using these antibodies. Our results also underscore the critical importance of the characterization and validation of antibodies before their use in mechanistic studies and as diagnostic and therapeutic agents. This will not only improve the quality and reproducibility of research and reduce costs but will also reduce the number of therapeutic antibody failures in the clinic.

neuroscience

Patient Similarity Network of Newly Diagnosed Multiple Myeloma Identifies Patient Sub-groups with Distinct Genetic Features and Clinical Implications

The remarkable genetic heterogeneity of Multiple Myeloma (MM) poses a significant challenge for proper prognostication and clinical management of patients. Accurate dissection of the genetic and molecular landscape of the disease and the robust identification of homogeneous classes of patients are essential steps to reliable risk stratification and the development of novel precision medicine strategies. Here we introduce MM-PSN, the first multi-omics Patient Similarity Network of newly diagnosed MM. MM-PSN has enabled the identification of three broad patient groups and twelve distinct sub-groups defined by five data types generated from genomic and transcriptomic patient profiling of 655 patients. The MM-PSN classification uncovered novel associations between distinct MM hallmarks with significant prognostic implications and allowed further refinement of risk stratification. Our analysis revealed that gain of 1q is the most important single lesion conferring high risk of relapse, and its association with an MMSET translocation is the most significant determinant of poor outcome. We developed a classifier and validated these results in an independent dataset of 559 pts. Our findings suggest that gain of 1q should be incorporated in routine staging systems and risk assessment tools. The MM-PSN classifier is available as a free resource to allow for an easy implementation in most clinical settings.

cancer biology