Stability engineering of the human antibody repertoire

AbstractHuman monoclonal antibodies often display limited thermodynamic and colloidal stabilities. This behavior hinders their production, and places limitations on the development of novel formulation conditions and therapeutic applications. Antibodies are highly diverse molecules, with much of the sequence variation observed within variable domain families and, in particular, their complementarity determining regions. This has complicated the development of comprehensive strategies for the stability engineering of the human antibody repertoire. Here we provide an overview of the field, and discuss recent advances in the development of robust and aggregation resistant antibody therapeutics

Autonomous Extraction of Millimeter-scale Deformation in InSAR Time Series Using Deep Learning

Systematic characterization of slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale every few days, may hold the key to those interactions. However, atmospheric propagation delays often exceed ground deformation of interest despite state-of-the art processing, and thus InSAR analysis requires expert interpretation and a priori knowledge of fault systems, precluding global investigations of deformation dynamics. Here we show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault's location or slip behaviour. Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm detection, revealing a slow earthquake twice as extensive as previously recognized. We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California

Increased mean aliphatic lipid chain length in left ventricular hypertrophy secondary to arterial hypertension: A cross-sectional study

About 77.9 million (1 in 4) American adults have high blood pressure. High blood pressure is the primary cause of left ventricular hypertrophy (LVH), which represents a strong predictor of future heart failure and cardiovascular mortality. Previous studies have shown an altered metabolic profile in hypertensive patients with LVH. The goal of this study was to identify blood metabolomic LVH biomarkers by H NMR to provide novel diagnostic tools for rapid LVH detection in populations of hypertensive individuals. This cross-sectional study included 48 hypertensive patients with LVH matched with 48 hypertensive patients with normal LV size, and 24 healthy controls. Two-dimensional targeted M-mode echocardiography was performed to measure left ventricular mass index. Partial least squares discriminant analysis was used for the multivariate analysis of the H NMR spectral data. From the H NMR-based metabolomic profiling, signals coming from methylene (-CH2-) and methyl (-CH3) moieties of aliphatic chains from plasma lipids were identified as discriminant variables. The -CH2-/-CH3 ratio, an indicator of the mean length of the aliphatic lipid chains, was significantly higher (P < 0.001) in the LVH group than in the hypertensive group without LVH and controls. Receiver operating characteristic curve showed that a cutoff of 2.34 provided a 52.08% sensitivity and 85.42% specificity for discriminating LVH (AUC = 0.703, P-value < 0.001). We propose the -CH2-/-CH3 ratio from plasma aliphatic lipid chains as a biomarker for the diagnosis of left ventricular remodeling in hypertension

Structure-based design of a highly stable, covalently-linked SARS-CoV-2 spike trimer with improved structural properties and immunogenicity

The continued threat of SARS-CoV-2 to global health necessitates development of improved research tools and vaccines. We present an improved SARS-CoV-2 spike ectodomain, “VFLIP”, bearing five proline substitutions, a flexible cleavage site linker, and an inter-protomer disulfide bond. VFLIP displays significantly improved stability, high-yield production and retains its trimeric state without exogenous trimerization motifs. High-resolution cryo-EM and glycan profiling reveal that the VFLIP quaternary structure and glycosylation mimic the native spike on the viral surface. Further, VFLIP has enhanced affinity and binding kinetics relative to other stabilized spike proteins for antibodies in the Coronavirus Immunotherapeutic Consortium (CoVIC), and mice immunized with VFLIP exhibit potent neutralizing antibody responses against wild-type and B.1.351 live SARS-CoV-2. Taken together, VFLIP represents an improved tool for diagnostics, structural biology, antibody discovery, and vaccine design.Competing Interest StatementThe authors have declared no competing interest

Blood Signature of Pre-Heart Failure: A Microarrays Study

International audienceBACKGROUND: The preclinical stage of systolic heart failure (HF), known as asymptomatic left ventricular dysfunction (ALVD), is diagnosed only by echocardiography, frequent in the general population and leads to a high risk of developing severe HF. Large scale screening for ALVD is a difficult task and represents a major unmet clinical challenge that requires the determination of ALVD biomarkers. METHODOLOGY/PRINCIPAL FINDINGS: 294 individuals were screened by echocardiography. We identified 9 ALVD cases out of 128 subjects with cardiovascular risk factors. White blood cell gene expression profiling was performed using pangenomic microarrays. Data were analyzed using principal component analysis (PCA) and Significant Analysis of Microarrays (SAM). To build an ALVD classifier model, we used the nearest centroid classification method (NCCM) with the ClaNC software package. Classification performance was determined using the leave-one-out cross-validation method. Blood transcriptome analysis provided a specific molecular signature for ALVD which defined a model based on 7 genes capable of discriminating ALVD cases. Analysis of an ALVD patients validation group demonstrated that these genes are accurate diagnostic predictors for ALVD with 87% accuracy and 100% precision. Furthermore, Receiver Operating Characteristic curves of expression levels confirmed that 6 out of 7 genes discriminate for left ventricular dysfunction classification. CONCLUSIONS/SIGNIFICANCE: These targets could serve to enhance the ability to efficiently detect ALVD by general care practitioners to facilitate preemptive initiation of medical treatment preventing the development of HF

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

An exponential build-up in seismic energy suggests a months-long nucleation of slow slip in Cascadia

International audienceSlow slip events result from the spontaneous weakening of the subduction megathrust and bear strong resemblance to earthquakes, only slower. This resemblance allows us to study fundamental aspects of nucleation that remain elusive for classic, fast earthquakes. We rely on machine learning algorithms to infer slow slip timing from statistics of seismic waveforms. We find that patterns in seismic power follow the 14-month slow slip cycle in Cascadia, arguing in favor of the predictability of slow slip rupture. Here, we show that seismic power exponentially increases as the slowly slipping portion of the subduction zone approaches failure, a behavior that shares a striking similarity with the increase in acoustic power observed prior to laboratory slow slip events. Our results suggest that the nucleation phase of Cascadia slow slip events may last from several weeks up to several months

Adrenomedullin inhibits adipogenesis under transcriptional control of insulin.

International audienceWe generated preadipocyte cell lines impaired in adrenomedullin production through integration of an adrenomedullin small interfering RNA expression vector. The reduction of adrenomedullin synthesis strongly accelerated adipose differentiation. These results were bolstered when overexpression of active adrenomedullin peptide led to delayed differentiation. Therefore, we propose that adrenomedullin is an antiadipogenic factor. Moreover, we checked whether insulin, a proadipogenic factor, regulates expression of adrenomedullin. We observed that insulin had an inhibitory effect on adrenomedullin expression in isolated human adipocyte cells. This response was dose dependent and was reversed by resistin, a new anti-insulin agent. We quantified circulating adrenomedullin in healthy obese patients and observed a threefold increase of adrenomedullin compared with lean patients. Furthermore, adrenomedullin plasma levels are negatively correlated to plasma insulin levels in these obese patients. The insulin inhibitory response was also observed in vivo in Sprague-Dawley rats but not in the insulin-resistant Zucker rat, suggesting that adrenomedullin expression is upregulated in insulin-resistant adipose cells. Using adrenomedullin promoter-luciferase reporter gene constructs, we have shown that the adrenomedullin response to insulin is mediated by insulin-responsive elements. These findings provide new insight into fat mass development and the relationship between obesity and elevated circulating adrenomedullin levels in diabetic patients