Biology subjects

Gonzalez, C.

Publications and source records attributed to Gonzalez, C..

5 recordsLinked to original sources

bioRxiv2018

Nanobody-antigen catch-bond reveals NK cell mechanosensitivity

Antibodies are key tools in biomedical research and medicine. Their binding properties are classically measured in solution and characterized by an affinity. However, in physiological conditions, antibodies can bridge an immune effector cell and an antigen presenting cell, implying that mechanical forces apply to the bonds. For example, in antibody-dependent cell cytotoxicity, a major mode of action of therapeutic monoclonal antibodies, the Fab domains bind the antigens on the target cell, while the Fc domain binds to the activating receptor CD16 (also known as FcgRIII) of an immune effector cell, in a quasi bi-dimensional environment (2D). Therefore, there is a strong need to investigating antigen/antibody binding under force (2D), to better understand and predict antibody activity in vivo. We used two anti-CD16 nanobodies targeting two different epitopes and laminar flow chamber assay to measure the association and dissociation of single bonds formed between microsphere-bound CD16 antigens and surface-bound anti-CD16 nanobodies (or single domain antibodies), simulating 2D encounters. The two nanobodies exhibit similar 2D association kinetics, characterized by a strong dependence on the molecular encounter duration. However, their 2D dissociation kinetics strongly differ as a function of applied force: one exhibits a slip bond behaviour where off-rate increases with force; the other exhibits a catch bond behaviour with off-rate decreasing with force. This is the first time, to our knowledge, that catch bond behaviour was reported for antigen-antibody bond. We further exploit this property to show how Natural Killer cells spread differentially on surfaces coated with these molecules, revealing NK cells mechanosensitivity. Our results may also have strong implications for the design of efficient bispecific antibodies for therapeutic applications.

biophysics↗

bioRxiv2018

Quantitative Assessment of Protein Activity in Orphan Tissues and Single Cells Using the metaVIPER Algorithm

We and others have shown that transition and maintenance of biological states is controlled by master regulator protein, which can be inferred by interrogating tissue-specific regulatory models (interactomes) with transcriptional signatures, using the VIPER algorithm. Yet, some tissues may lack molecular profiles necessary for interactome inference (orphan tissues), or, as for single cells isolated from heterogeneous samples, their tissue context may be undetermined. To address this problem, we introduce metaVIPER, a novel algorithm designed to assess protein activity in tissue-independent by integrative analysis of multiple, non-tissue-matched interactomes. This assumes that transcriptional targets of each protein will be recapitulated by one or more available interactome. We confirmed the algorithms value in assessing protein dysregulation induced by somatic mutations, as well as in assessing protein activity in orphan tissues and, most critically, in single cells, thus allowing transformation of noisy and potentially biased RNA-Seq signatures into reproducible protein-activity signatures.

systems biology↗

bioRxiv2017

Barcoding analysis of HIV drug resistance mutations using Oxford Nanopore MinION (ONT) sequencing

Determination of HIV drug resistance (HIVDR) is becoming an integral baseline HIV evaluation for newly infected subjects, as the level of pre-treatment resistance is increasing worldwide. Until now, the gold standard for monitoring ART mutations is the Sanger sequencing method, however, next-generation sequencing technologies (NGS) because high-throughput capability, are gaining attention as a method for detection of HIVDR. In the present work, we evaluated the use of the Oxford Nanopore Technologies (ONT) MinION as an alternative method for detection of drug resistance mutations in pre-treatment HIV positive subjects.\n\nWe evaluate 36 samples taken during November 2016 from treatment naive subjects with age greater than 18 years old, who went to the lab for their first HIV monitoring. To evaluate the agreement between Sanger and MinION generated sequences, we aligned the sequences ([~]1200bp) with muscle v. 3.8.31. Then we counted the differences and calculated the p-distance of the obtained sequences, comparing paired sequences and grouping Sanger and MinION obtained sequences. The percentage of similarity among each sequence was also evaluated.\n\nAll samples were submitted to the Standford University HIV drug resistance database (HIVdb version 8.4). Then we compared the resistance predictions obtained from the sequences generated by Sanger and MinION methods.\n\nResults: The median of available pores was 1314 for the first run, 1215 for the second run, and 536 for the third run. After 3 hours with SQK-NSK007 a total of 18803 2D reads were base-called and in 16577 reads (88%) a barcode was detected.\n\nComparing the nucleotide differences of each sample, we observed that 23 (74%) samples had identical sequence, for the other samples the percentage of identity among each analyzed sequence was greater than 95%. A good positive predictive value (100%) in the estimation of drug resistance mutations in the groups of protease inhibitors (PI), nucleoside reverse transcriptase inhibitors (NRTIs), and non-nucleoside reverse transcriptase inhibitors (NNRTIs).\n\nWe present an approach for the analysis of HIV reads generated with MinION ONT, further studies are guaranteed before the application of this methodology in clinical settings to assess its suitability for HIVDR testing.

genomics↗

bioRxiv2017

PAIRUP-MS: Pathway Analysis and Imputation to Relate Unknowns in Profiles from Mass Spectrometry-based metabolite data

Metabolomics is a powerful approach for discovering biomarkers and metabolic quantitative trait loci. While untargeted profiling methods can measure up to thousands of metabolite signals in a single experiment, many signals cannot be readily identified as known metabolites or compared across datasets, making it difficult to infer biology and to conduct well-powered meta-analyses across studies. To deal with these challenges, we developed a suite of computational methods, PAIRUP-MS, to match metabolite signals across mass spectrometry-based profiling datasets using an imputation-based approach and to generate pathway annotations for these signals. We performed meta and pathway analyses for both known and unknown signals in multiple datasets and then validated the results using genetic associations. Finally, we applied the methods to detect metabolite signals and pathways associated with body mass index, demonstrating that our framework is useful for analyzing unknown signals in a robust and biologically meaningful manner and for improving the power of untargeted metabolomics studies.

bioinformatics↗

bioRxiv2017

Drosophila larval brain neoplasms present tumour-type dependent genome instability

Single nucleotide polymorphisms (SNPs) and copy number variants (CNVs) are found at different rates in human cancer. To determine if these genetic lesions appear in Drosophila tumours we have sequenced the genomes of 17 malignant neoplasms caused by mutations in l(3)mbt, brat, aurA, or lgl. We have found CNVs and SNPs in all the tumours. Tumour-linked CNVs range between 11 and 80 per sample, affecting between 92 and 1546 coding sequences. CNVs are in average less frequent in l(3)mbt than in brat lines. Nearly half of the CNVs fall within the 10 to 100Kb range, all tumour samples contain CNVs larger that 100 Kb and some have CNVs larger than 1Mb. The rates of tumour-linked SNPs change more than 20-fold depending on the tumour type: late stage brat, l(3)mbt, and aurA and lgl lines present median values of SNPs/Mb of exome of 0.16, 0.48, and 3.6, respectively. Higher SNP rates are mostly accounted for by C>A transversions, which likely reflect enhanced oxidative stress conditions in the affected tumours. Both CNVs and SNPs turn over rapidly. We found no evidence for selection of a gene signature affected by CNVs or SNPs in the cohort. Altogether, our results show that the rates of CNVs and SNPs, as well as the distribution of CNV sizes in this cohort of Drosophila tumours are well within the range of those reported for human cancer. Genome instability is therefore inherent to Drosophila malignant neoplastic growth at a variable extent that is tumour type dependent.\n\nAUTHOR SUMMARYDrosophila models of malignant growth can help to understand the molecular mechanisms of malignancy. These models are known to exhibit some of the hallmarks of cancer like sustained growth, immortality, metabolic reprogramming, and others. However, it is currently unclear if these fly models are affected by genome instability, which is another hallmark of many human malignant tumours. To address this issue we have sequenced and analysed the genomes of a cohort of seventeen fly tumour samples. We have found that genome instability is a common trait of Drosophila malignant tumours, which occurs at an extent that is tumour-type dependent, at rates that are similar to those of different human cancers.

cancer biology↗