Alejandro E. Brito

Polylactosaminoglycan Glycomics: Enhancing the Detection of High-molecular-weight N-glycans in Matrix-assisted Laser Desorption Ionization Time-of-flight Profiles by Matched Filtering

For over 30 years, protocols based on the mass spectrometry (MS) of permethylated derivatives, complemented by enzymatic degradations, have underpinned glycomic experiments aimed at defining the structures of individual glycans present in the complex mixtures that are characteristic of biological samples. Both MS instrumentation and sample handling have improved markedly in recent years, enabling greater sensitivity and better signal-to-noise ratios, thereby facilitating the detection of glycans at much higher masses than could be achieved in the past. The latter is especially important for the characterization of the biologically important class of N-glycans that carry polylactosaminoglycan chains. Such advances in data acquisition heighten the need for informatics tools to assist in glycan structure assignment. Here, utilizing mouse lung tissue as a model system, we present evidence of polylactosaminoglycan-containing N-glycans with permethylated molecular weights exceeding 13 kDa. We show that antennae branching patterns and lengths can be successfully determined at these high masses via MS/MS experiments, even when MS ion counts are very low. We also describe the development and application of a matched filtering algorithm for assisting high-molecular-weight glycan detection and structure assignment.

Achieving Differential Privacy in Secure Multiparty Data Aggregation Protocols on Star Networks

We consider the problem of privacy-preserving data aggregation in a star network topology, i.e., several untrusting participants connected to a single aggregator. We require that the participants do not discover each others data, and the service provider remains oblivious to each participants individual contribution. Furthermore, the final result is to be published in a differentially private manner, i.e., the result should not reveal the contribution of any single participant to a (possibly external) adversary who knows the contributions of all other participants. In other words, we require a secure multiparty computation protocol that also incorporates a differentially private mechanism. Previous solutions have resorted to caveats such as postulating a trusted dealer to distribute keys to the participants, or introducing additional entities to withhold the decryption key from the aggregator, or relaxing the star topology by allowing pairwise communication amongst the participants. In this paper, we show how to obtain a noisy (differentially private) aggregation result using Shamir secret sharing and additively homomorphic encryption without these mitigating assumptions. More importantly, while we assume semi-honest participants, we allow the aggregator to be stronger than semi-honest, specifically in the sense that he can try to reduce the noise in the differentially private result. To respect the differential privacy requirement, collusions of mutually untrusting entities need to be analyzed differently from traditional secure multiparty computation: It is not sufficient that such collusions do not reveal the data of honest participants; we must also ensure that the colluding entities cannot undermine differential privacy by reducing the amount of noise in the final result. Our protocols avoid this by requiring that no entity -- neither the aggregator nor any participant -- knows how much noise a participant contributes to the final result. We also ensure that if a cheating aggregator tries to influence the noise term in the differentially private output, he can be detected with overwhelming probability.

Privacy, efficiency & fault tolerance in aggregate computations on massive star networks

We consider the challenge of performing efficient, fault-tolerant, privacy-preserving aggregate computations in a star topology, i.e., a massive number of participants connected to a single untrusted aggregator. The privacy constraints are that the participants do not discover each others data, and the aggregator obtains the final results while remaining oblivious to each participants individual contribution to the aggregate. In achieving these goals, previous approaches have either assumed a trusted dealer that distributes keys to the participants and the aggregator, or introduced additional parties that withhold the decryption key from the aggregator, or applied secret sharing with either pairwise communication amongst the participants or O(N2) ciphertext overhead at the aggregator. In contrast, we describe a protocol based on Shamir secret sharing and homomorphic encryption without assuming any additional parties. We also eliminate all pairwise communication amongst the participants and still require only O(N1+ε) overhead at the aggregator, where ε ≪ 1 can be achieved for massively multiparty computation. Our protocol arranges the star-connected participants into a logical hierarchy that facilitates parallelization, while allowing for user churn, i.e., a specified number of participants can go offline after providing their data, and new participants can join at a later stage of the computation.

Benchmark study of automatic annotation of MALDI-TOF N-glycan profiles.

Human experts can annotate peaks in MALDI-TOF profiles of detached N-glycans with some degree of accuracy. Even though MALDI-TOF profiles give only intact masses without any fragmentation information, expert knowledge of the most common glycans and biosynthetic pathways in the biological system can point to a small set of most likely glycan structures at the cartoon level of detail. Cartoonist is a recently developed, fully automatic annotation tool for MALDI-TOF glycan profiles. Here we benchmark Cartoonists automatic annotations against human expert annotations on human and mouse N-glycan data from the Consortium for Functional Glycomics. We find that Cartoonist and expert annotations largely agree, but the expert tends to annotate more specifically, meaning fewer suggested structures per peak, and Cartoonist more comprehensively, meaning more annotated peaks. On peaks for which both Cartoonist and the expert give unique cartoons, the two cartoons agree in over 90% of all cases. This article is part of a Special Issue entitled: Computational Proteomics.