Biology subjects

Silberberg, M.

Publications and source records attributed to Silberberg, M..

6 recordsLinked to original sources

bioRxiv2025

Oral-stomach sampling to replace rumen-fistulated animals in ruminant nutrition research - a case study

Studies using fistulated ruminants have shed light on digestion and fermentation in the rumen and yielded tools to determine the feed efficiency of diets, limit effluents or anticipate variability in the quality of animal products. However, the ethical acceptability of such studies has been called into question. Our objective was to determine whether oral-stomach sampling (OSS) is an acceptable alternative to sampling through a ruminal cannula in characterizing the variability of rumen fluid composition induced by an acidogenic dietary challenge in dairy cows. During three four-week periods (P1, P2, P3), six rumen-fistulated cows were fed a standard diet based on supplemented corn silage (periods 1 and 3) alternating with a diet enriched with starch (period 2). Rumen juice was collected through cannulas at three locations in the rumen (reticulum, ventral sac or a mix of both) and by OSS, once per week at 0830 h, i.e. before morning feeding, and once every third week of each period at 1330 h, i.e. 4.5 hours after morning feeding. Whatever the sampling method or location, ruminal pH was lower in period 2 compared to periods 1 and 3 at 1330 h (6.21 vs. 5.57 vs. 6.12 in periods 1, 2 and 3). Ruminal pH was higher when obtained by OSS rather than cannulation, whatever the sampling location (on average, +0.44 points at 0830 h and +0.56 points at 1330 h). Mineral composition indicated a presumed dilution by saliva of OSS samples. This was also consistent with lower concentrations of volatile fatty acids. The next steps will be to analyze the associated variations in in vitro rumen fermentation parameters and microbiota composition. ImplicationsThe use of fistulated ruminants to elucidate ruminal physiology is currently controversial in society. In order to minimise, reduce or replace these experimental models, alternatives to ruminal fistula sampling are being evaluated. Here we focus on oral stomach sampling (OSS). We show that OSS can be a satisfactory alternative to cannula sampling in the evaluation of rumen fermentation parameters, especially for the molar proportion of volatile fatty acids, rumen pH, and rumen volatile fatty acid and ammonia concentrations. We also show that OSS must be standardized to avoid salivary contamination affecting the quality of the results.

physiology↗

bioRxiv2024

Poincare and SimBio: a versatile and extensible Python ecosystem for modeling systems.

Chemical Reaction Networks (CRNs) play a pivotal role in diverse fields such as systems biology, biochemistry, chemical engineering, and epidemiology. High-level modelling of CRNs enables various simulation approaches, including deterministic and stochastic methods. However, existing Python tools for CRN modelling typically wrap external C/C++ libraries for modelling and simulation, limiting their extensibility and integration with the broader Python ecosystem. In response, we developed Poincare and SimBio, two novel Python packages for the definition and simulation of dynamical systems and CRNs. Poincare serves as a foundation for dynamical system modelling, while SimBio extends this functionality to CRNs, including support for the Systems Biology Markup Language (SBML). Poincare and SimBio are developed as pure Python packages enabling users to easily extend their simulation capabilities by writing new or leveraging other Python packages. Moreover, this does not compromise the performance, as code can be Just-In-Time compiled with Numba. Our benchmark tests using curated models from the BioModels repository, demonstrate that these tools may provide a potentially superior performance advantage, compared to other existing tools. Additionally, to ensure a user-friendly experience, our packages use standard typed modern Python syntax that provides a seamless integration with Integrated Development Environments (IDEs). Python-centric approach significantly enhances code analysis, error detection, and refactoring capabilities, positioning Poincare and SimBio as valuable tools for the modelling community.

bioinformatics↗

bioRxiv2021

Robust and unbiased estimation of the background distribution for automated quantitative imaging

Quantitative analysis of high-throughput microscopy images requires robust automated algorithms. Background estimation is usually the first step and has an impact on all subsequent analysis, in particular for foreground detection and calculation of ratiometric quantities. Most methods recover only a single background value, such as the median. Those that aim to retrieve a background distribution by dividing the intensity histogram yield a biased estimation in images in non-trivial cases. In this work, we present the first method to recover an unbiased estimation of the background distribution directly from an image and without any additional input. Through a robust statistical test, our method leverages the lack of local spatial correlation in background pixels to select a subset of pixels that accurately represent the background distribution. This method is both fast and simple to implement, as it only uses standard mathematical operations and an averaging filter. Additionally, the only parameter, the size of the averaging filter, does not require fine tuning. The obtained background distribution can be used to test for foreground membership of individual pixels, or to estimate confidence intervals in derived quantities. We expect that the concepts described in this work can help to develop a novel family of robust segmentation methods.

cell biology↗

bioRxiv2021

An Integrative Apoptotic Reaction Model for extrinsic and intrinsic stimuli

Apoptosis, a form of programmed cell death central to all multicellular organisms, plays a key role during organism development and is often misregulated in cancer. Devising a single model applicable to distinct stimuli and conditions has been limited by lack of robust observables. Indeed, previous numerical models have been tailored to fit experimental datasets in restricted scenarios, failing to predict response to different stimuli. We quantified the activity of three caspases simultaneously upon intrinsic or extrinsic stimulation to assemble a comprehensive dataset. We measured and modeled the time between maximum activity of intrinsic, extrinsic and effector caspases, a robust observable of network dynamics, to create the first integrated Apoptotic Reaction Model (ARM). Observing how effector caspases reach maximum activity first irrespective of stimuli used, led us to identify and incorporate a missing feedback into a successful model for extrinsic stimulation. By simulating different recently performed experiments, we corroborated that ARM adequately describes them. This integrated model provides further insight into the indispensable feedback from effector caspase to initiator caspases.

systems biology↗

bioRxiv2021

CASPAM: a triple modality biosensor for multiplexed imaging of caspase network activity

Understanding signal propagation across biological networks requires to simultaneously monitor the dynamics of several nodes to uncover correlations masked by inherent intercellular variability. To monitor the enzymatic activity of more than two components over short time scales has proven challenging. Exploiting the narrow spectral width of homoFRET-based biosensors, up to three activities can be imaged through fluorescence polarization anisotropy microscopy. We introduce CASPAM (Caspase Activity Sensor by Polarization Anisotropy Multiplexing) a single-plasmid triple-modality-reporter of key nodes of the apoptotic network. Apoptosis provides an ideal molecular framework to study interactions between its three composing pathways (intrinsic, extrinsic and effector). We characterized the biosensor performance and demonstrated the advantages that equimolar expression has both in simplifying experimental procedure and reducing observable variation, thus enabling robust data-driven modelling. Tools like CASPAM become essential to analyze molecular pathways where multiple nodes need to be simultaneously monitored.

cell biology↗

bioRxiv2018

Changes in rumen microbiota of cows in response to dietary supplementation with nitrate, linseed and saponin alone or in combination.

Dietary supplementation with linseed, saponins and nitrate is a promising methane mitigation strategy in ruminant production. The main objective of this work was to assess the effects of these additives on the rumen microbiota in order to understand underlying microbial mechanisms of methane abatement. Two 2 x 2 factorial design studies were conducted simultaneously, which also allowed us to make a broad-based assessment of microbial responses. Eight non-lactating cows were fed diets supplemented with linseed or saponin in order to decrease hydrogen production and nitrate to deviate hydrogen consumption; also, combinations of linseed plus nitrate or saponin plus nitrate were used to explore the interaction between dietary treatments. Amplicon sequencing of 18S and 16S rRNA genes was employed to characterise rumen microbes. Nitrate fed alone or in combination in both studies dramatically affected the composition and structure of rumen microbiota, though impacts were more evident in one of the studies. Linseed moderately modified bacterial community structure with no effect on rumen methanogens and protozoa. Indicator OTU analysis revealed that both linseed and nitrate reduced the relative abundance of hydrogen-producing Ruminococcaceae. Linseed increased the proportion of bacteria known to reduce succinate to propionate, whereas nitrate supplementation increased nitrate-reducing bacteria and decreased the metabolic activity of rumen methanogens. Saponins had no effect on the microbiota. Inconsistency found between the two studies, when nitrate was fed to the cows could be explained by changes in microbial ecosystem functioning rather than changes in microbial community structure.\n\nImportanceThis study aimed at identifying the microbial mechanisms of enteric methane mitigation when linseed, nitrate and saponins were fed to non-lactating cows alone or in a combination. Hydrogen is a limiting factor in rumen methanogenesis. We hypothesised that linseed and saponins would affect hydrogen producers and nitrate would deviate hydrogen consumption thus leading to reduced methane production in the rumen. Contrary to what was foreseen, both linseed and nitrate had a deleterious effect on hydrogen producers; linseed also redirected hydrogen consumption towards propionate production, whereas nitrate stimulated the growth of nitrate reducing and hence hydrogen-consuming bacterial taxa. Fundamental knowledge of microbial mechanism involved in rumen methanogenesis, provides novel insights for the development of new or the optimisation of existing methane mitigation strategies.

molecular biology↗