Miracle-dz/UniFAR-FTUs huggingface dataset:
A Highly Sensitive Fluorogenic Assay for the Detection of Nephrotoxin-Induced Oxidative Stress in Live Cells and Renal Tissue

A common manifestation of drug toxicity is kidney injury or nephrotoxicity. Nephrotoxicity frequently leads to termination of clinical trials and drug withdrawals, which jeopardize biomedical progress. Efficient preclinical screening platforms capable of detecting mild signs of kidney damage, which may elicit considerable toxic response in vivo, are essential. A common manifestation of chemical toxicity is oxidative modification of cellular biomolecules. Therefore, we have developed a facile biomolecule carbonyl detection assay that well surpasses the sensitivity of the standard assays in identifying modest forms of renal injury. Using a novel fluorogenic sensor, TFCH, we have demonstrated the applicability of the assay in live kidney cells and in renal tissue. This robust assay can help inform preclinical decisions to recall unsafe drug candidates. Application of this assay in identifying and analyzing diverse pathologies is envisioned.
Brief sensory deprivation triggers plasticity of dopamine‐synthesising enzyme expression in genetically labelled olfactory bulb dopaminergic neurons

In the glomerular layer of the olfactory bulb, local dopaminergic interneurons play a key role in regulating the flow of sensory information from nose to cortex. These dual dopamine- and GABA-releasing cells are capable of marked experience-dependent changes in the expression of neurotransmitter-synthesising enzymes, including tyrosine hydroxylase (TH). However, such plasticity has most commonly been studied in cell populations identified by their expression of the enzyme being studied, and after long periods of sensory deprivation. Here, instead, we used brief 1- or 3-day manipulations of olfactory experience in juvenile mice, coupled with a conditional genetic approach that labelled neurons contingent upon their expression of the dopamine transporter (DAT-tdTomato). This enabled us to evaluate the potential for rapid changes in neurotransmitter-synthesising enzyme expression in an independently identified neuronal population. Our labelling strategy showed good specificity for olfactory bulb dopaminergic neurons, whilst revealing a minority sub-population of non-dopaminergic DAT-tdTomato cells that expressed the calcium-binding protein calretinin. Crucially, the proportions of these neuronal subtypes were not affected by brief alterations in sensory experience. Short-term olfactory manipulations also produced no significant changes in immunofluorescence or whole-bulb mRNA for the GABA-synthesising enzyme GAD67/Gad1. However, in bulbar DAT-tdTomato neurons brief sensory deprivation was accompanied by a transient, small drop in immunofluorescence for the dopamine-synthesising enzyme dopa decarboxylase (DDC), and a sustained decrease for TH. Deprivation also produced a sustained decrease in whole-bulb Th mRNA. Careful characterisation of an independently identified, genetically labelled neuronal population therefore enabled us to uncover rapid experience-dependent changes in dopamine-synthesising enzyme expression.
Analysis of coagulation factor IX in bioreactor cell culture medium predicts yield and quality of the purified product

Coagulation factor IX (FIX) is a highly complex post-translationally modified human serum glycoprotein and a high-value biopharmaceutical. The quality of recombinant FIX (rFIX), especially complete γ-carboxylation, is critical for rFIX clinical efficacy. Changes in bioreactor operating conditions can impact rFIX production and occupancy and structure of rFIX post-translational modifications (PTMs). We hypothesized that monitoring the bioreactor cell culture supernatant with Data Independent Acquisition Mass Spectrometry (DIA-MS) proteomics would allow us to predict product yield and quality after purification. With the goal of optimizing rFIX production, we developed a suite of MS proteomics analytical methods and used these to investigate changes in rFIX yield, γ-carboxylation, other PTMs, and host cell proteins during bioreactor culture and after purification. Our methods provided a detailed overview of the dynamics of site-specific PTM occupancy and abundance on rFIX during production, which accurately predicted the efficiency of purification and the quality of the purified product from different culture conditions. In addition, we identified new PTMs in rFIX, some of which were near the GLA domain and could impact rFIX GLA-dependent purification efficiency and protein function. The workflows presented here are applicable to other biologics and expression systems, and should aid in the optimization and quality control of upstream and downstream bioprocesses.
Asymmetric phospholipids impart novel biophysical properties to lipid bilayers allowing environmental adaptation

Phospholipids are a diverse group of biomolecules consisting of a hydrophilic head group and two hydrophobic acyl tails. The nature of the head and length and saturation of the acyl tails are important for defining the biophysical properties of lipid bilayers. It has recently been shown that the membranes of certain yeast species contain high levels of unusual asymmetric phospholipids, consisting of one long and one medium chain acyl moiety – a configuration not common in mammalian cells or other well studied model yeast species. This raises the possibility that structurally asymmetric phospholipids impart novel biophysical properties to the yeast membranes. Here, we use atomistic molecular dynamics simulations (MD) and environmentally-sensitive fluorescent membrane probes to characterize key biophysical parameters of membranes formed from asymmetric lipids for the first time. Interestingly, we show that saturated, but asymmetric phospholipids maintain membrane lipid order across a wider range of temperatures and do not require acyl tail unsaturation or sterols to maintain their properties. This may allow cells to maintain membrane fluidity even in environments which lack the oxygen required for the synthesis of unsaturated lipids and sterols.
Diverse viruses in deep-sea hydrothermal vent fluids have restricted dispersal across ocean basins

In the ocean, viruses impact microbial mortality, regulate biogeochemical cycling, and alter the metabolic potential of microbial lineages. At deep-sea hydrothermal vents, abundant viruses infect a wide range of hosts among the archaea and bacteria that inhabit these dynamic habitats. However, little is known about viral diversity, host range, and biogeography across different vent ecosystems, which has important implications for how viruses manipulate microbial function and evolution. Here, we examined viral diversity, viral and host distribution, and viral-host interactions in microbial metagenomes generated from venting fluids from several vent sites within three different geochemically and geographically distinct hydrothermal systems: Piccard and Von Damm vent fields at the Mid-Cayman Rise in the Caribbean Sea, and at several vent sites within Axial Seamount in the Pacific Ocean. Analysis of viral sequences and Clustered Regularly InterSpaced Palindromic Repeats (CRISPR) spacers revealed highly diverse viral assemblages and evidence of active infection. Network analysis revealed that viral host range was relatively narrow, with very few viruses infecting multiple microbial lineages. Viruses were largely endemic to individual vent sites, indicating restricted dispersal, and in some cases viral assemblages persisted over time. Thus, we show that hydrothermal vent fluids are home to novel, diverse viral assemblages that are highly localized to specific regions and taxa. Importance Viruses play important roles in manipulating microbial communities and their evolution in the ocean, yet not much is known about viruses in deep-sea hydrothermal vents. However, viral ecology and evolution are of particular interest in hydrothermal vent habitats because of their unique nature: previous studies have indicated that most viruses in hydrothermal vents are temperate rather than lytic, and it has been established that rates of horizontal gene transfer (HGT) are particularly high among thermophilic vent microbes, and viruses are common vectors for HGT. If viruses have broad host range or are widespread across vent sites, they have increased potential to act as gene-sharing “highways” between vent sites. By examining viral diversity, distribution, and infection networks across disparate vent sites, this study provides the opportunity to better characterize and constrain the viral impact on hydrothermal vent microbial communities. We show that viruses in hydrothermal vents are diverse and apparently active, but most have restricted host range and are not widely distributed among vent sites. Thus, the impacts of viral infection are likely to be highly localized and constrained to specific taxa in these habitats.
Deep learning classification of lipid droplets in quantitative phase images

We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all machine learning methods that we implemented, and their performance in computational requirements, training resource needs, and accuracy. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity and deeper insight into the thermodynamics of metabolism of single cells. Author Summary Recently, quantitative-phase imaging (QPI) has demonstrated the ability to elucidate novel parameters of cellular physiology and metabolism without the need for fluorescent staining. Here, we apply label-free, low photo-toxicity QPI to yeast cells in order to identify lipid droplets (LDs), an important organelle with key implications in human health and biofuel development. Because QPI yields low specificity, we explore the use of modern machine learning methods to rapidly identify intracellular LDs with high discriminatory power and accuracy. In recent years, machine learning has demonstrated exceptional abilities to recognize and segment objects in biomedical imaging, remote sensing, and other areas. Trained machine learning classifiers can be combined with QPI within high-throughput analysis pipelines, allowing for efficient and accurate identification and quantification of cellular components. Non-invasive, accurate and high-throughput classification of these organelles will accelerate research and improve our understanding of cellular functions with beneficial applications in biofuels, biomedicine, and more.
Exploring the maturation of a monocytic cell line using self-organizing maps of single-cell Raman spectra

Phorbol myristate acetate (PMA)-differentiated THP-1 cells are routinely used in lieu of primary macrophages to study macrophage polarization during host-pathogen interactions and disease progression. The phenotypes of the THP-1 macrophages are influenced by the level and duration of PMA stimulation, and possibly also by the presence of adhesion factors. Here, we use self-organizing maps (SOMs) of single-cell Raman spectra to probe the effects of PMA stimulation conditions and adhesion factors on THP-1 cell differentiation. Raman spectra encoding for biochemical composition were acquired from individual cells on substrates coated with fibronectin or poly-L-lysine before and after stimulation with 20 nM or 200 nM PMA for two different time intervals. SOMs that show the extent of spectral dissimilarity were constructed. For all stimulation conditions, the SOMs indicated the spectra acquired from cells after 3 d treatment had diverged from those of untreated cells. The SOMs also showed treatment with 200 nM PMA for 3 d produced both fully and partially differentiated cells on both adhesion factors, whereas the outcome of 20 nM PMA treatment for 3 d depended on the adhesion factor. Treatment of THP-1 cells on fibronectin with 20 nM PMA for 3 d produced both partially and fully differentiated cells, but this treatment induced an intermediate stage of differentiation when applied to THP-1 cells on poly-L-lysine. Thus, the transition of THP-1 monocytes into macrophage-like cells may be modulated by integrin-binding interactions. Furthermore, the composite of culture and stimulation conditions may confound the comparison of results from separate studies.
Control of filament length by a depolymerizing gradient

Cells assemble microns-long filamentous structures from protein monomers that are nanometers in size. These structures are often highly dynamic, yet in order for them to function properly, cells maintain precise filament lengths. In this paper, we investigate length dependent depolymerization as a mechanism of length control. This mechanism has been recently proposed for flagellar length control in the single cell organisms Chlamydomonas and Giardia. Length dependent depolymerization can arise from a concentration gradient of a depolymerizing protein, such as kinesin-13 in Giardia, along the length of the flagellum. Two possible scenarios are considered, that of a linear and an exponential gradient of depolymerizing proteins. We compute analytically the probability distributions of filament lengths for both scenarios and show how these distributions are controlled by key biochemical parameters through a dimensionless number that we identify. In Chlamydomonas cells it has been well documented that the assembly dynamics of its two flagella are coupled via a shared pool of molecular components, and so we investigate the effect of a limiting monomer pool on the length distributions. Finally, we compare our calculations to experiments. While the computed mean lengths are consistent with observations, the noise is two orders of magnitude smaller than the observed length fluctuations.
Difructose Anhydride III Enhances Zinc Absorption in vivo: Zinc Gluconate is More Suitable for the Effects of Difructose Anhydride III than Zinc Yeast

We examined the effect of difructose anhydride III (DFAIII) on zinc absorption in rats in two experiments. First, we compared the absorption of zinc from zinc gluconate and zinc yeast with or without DFAIII by measuring concentrations in plasma collected from the portal vein after a single oral administration of zinc gluconate or zinc yeast with or without DFAIII. The peak plasma zinc concentration and the incremental area under the curve (IAUC) of plasma zinc 6 h after administration were highest in the zinc gluconate with DFAIII group. Second, we examined the effect of DFAIII on the absorption of zinc with phytic acid. with or without DFAIII. Plasma zinc levels in groups both with and without DFAIII were nearly the same as those at the baseline. Our results showed that DFAIII was better with zinc gluconate than zinc yeast for zinc absorption, and that DFAIII does not alter the inhibitory effects of phytic acid on zinc absorption.
Discovering compounds mimicking calorie restriction using mammalian gene expression profiles

Obesity is a risk factor for cardiovascular diseases, diabetes and cancer. In theory the obesity problem could be solve by the adherence to a calorie restricted diet, but that is not generally achieved in practice. An alternative is a pharmacological approach, using compounds that trigger the same metabolic changes associated with calorie restriction. Here I expand in the pharmacological direction by identifying compounds that induce liver gene signature profiles that mimic those induced by calorie restriction. Using gene expression profiles from mice and rat I identify corticosteroids, PPAR agonists and some antibacterial/antifungal as candidate compounds mimicking the response to calorie restriction in the liver gene signatures. Liver gene signature analysis can be used to identify compounds that mimic calorie restriction.
A population-genomic approach for estimating selection on polygenic traits in heterogeneous environments

Strong selection can cause rapid evolutionary change, but temporal fluctuations in the form, direction and intensity of selection can limit net evolutionary change over longer time periods. Fluctuating selection could affect molecular diversity levels and the evolution of plasticity and ecological specialization. Nonetheless, this phenomenon remains understudied, in part because of analytical limitations and the general difficulty of detecting selection that does not occur in a consistent manner. Herein, I fill this analytical gap by presenting an approximate Bayesian computation (ABC) method to detect and quantify fluctuating selection on poly-genic traits from population-genomic time-series data. I propose a model for environment-dependent phenotypic selection. The evolutionary genetic consequences of selection are then modeled based on a genotype-phenotype map. Using simulations, I show that the proposed method generates accurate and precise estimates of selection when the generative model for the data is similar to the model assumed by the method. Performance of the method when applied to an evolve-and-resequence study of host adaptation in the cowpea seed beetle (Cal-losobruchus maculatus) was more idiosyncratic and depended on specific analytical choices. Despite some limitations, these results suggest the proposed method provides a powerful approach to connect causes of (variable) selection to traits and genome-wide patterns of evolution. Documentation and open source computer software (fsabc) implementing this method are available from GitHub (https://github.com/zgompert/fsabc.git).
Fine Tuning of Calcium Constitutive Entry by Optogenetically-Controlled Membrane Polarization: Impact on Cell Migration

Anomalies in constitutive calcium entry (CCE) have been commonly attributed to cell dysfunction in pathological conditions such as cancer. Calcium influxes of this type rely on channels, such as TRP, to be constitutively opened and strongly depend on membrane potential and calcium driving force. We developed an optogenetic approach based on expression of the halorhodopsin chloride pump to study CCE in non-excitable cells. Using C2C12 cells, we found that halorhodopsin can be used to achieve a finely-tuned control of membrane polarization. Escalating the membrane polarization by incremental changes in light led to a concomitant increase in CCE through TRPV2 channels. Moreover, light-induced calcium entry through TRPV2 channels promoted cell migration. Our study shows for the first time that by modulating CCE and related physiological responses such as cell motility, halorhodopsin serves as a potentially powerful tool that could open new avenues for the study of CCE and associated cellular behaviors.
Extensive and diverse patterns of cell death sculpt neural networks in insects

Changes to the structure and function of neural networks are thought to underlie the evolutionary adaptation of animal behaviours. Among the many developmental phenomena that generate change programmed cell death appears to play a key role. We show that cell death occurs continuously throughout insect neurogenesis and happens soon after neurons are born. Focusing on two dipterans which have lost flight during evolution we reveal that reductions in populations of flight interneurons are likely caused by increased cell death during development. Mimicking an evolutionary role for increasing cell numbers, we artificially block programmed cell death in the medial neuroblast lineage in Drosophila melanogaster, which results in the production of ‘undead’ neurons with complex arborisations and distinct neurotransmitter identities. Activation of these ‘undead’ neurons and recordings of neural activity in behaving animals demonstrate that they are functional. Our findings suggest that the evolutionary modulation of death-based patterning could generate novel network configurations.
Dense Computer Replica of Cortical Microcircuits Unravels Cellular Underpinnings of Auditory Surprise Response

The nervous system is notorious for its strong response evoked by a surprising sensory input, but the biophysical and anatomical underpinnings of this phenomenon are only partially understood. Here we utilized in-silico experiments of a biologically-detailed model of a neocortical microcircuit to study stimulus specific adaptation (SSA) in the auditory cortex, whereby the neuronal response adapts significantly for a repeated (“expected”) tone but not for a rare (“surprise”) tone. SSA experiments were mimicked by stimulating tonotopically-mapped thalamo-cortical afferents projecting to the microcircuit; the activity of these afferents was modeled based on our in-vivo recordings from individual thalamic neurons. The modeled microcircuit expressed naturally many experimentally-observed properties of SSA, suggesting that SSA is a general property of neocortical microcircuits. By systematically modulating circuit parameters, we found that key features of SSA depended on synergistic effects of synaptic depression, spike frequency adaptation and recurrent network connectivity. The relative contribution of each of these mechanisms in shaping SSA was explored, additional SSA-related experimental results were explained and new experiments for further studying SSA were suggested.
Clustering of loci controlling species differences in male chemical bouquets of sympatric Heliconius butterflies

The degree to which loci promoting reproductive isolation cluster in the genome – i.e. the genetic architecture of reproductive isolation - can influence the tempo and mode of speciation. Tight linkage between these loci can facilitate speciation in the face of gene flow. Pheromones play a role in reproductive isolation in many Lepidoptera species, and the role of endogenously-produced compounds as secondary metabolites decreases the likelihood of pleiotropy associated with many barrier loci. Heliconius butterflies use male sex pheromones to both court females (aphrodisiac wing pheromones) and ward off male courtship (male-transferred anti-aphrodisiac genital pheromones), and it is likely that these compounds play a role in reproductive isolation between Heliconius species. Using a set of backcross hybrids between H. melpomene and H. cydno, we investigated the genetic architecture of putative male pheromone compound production. We found a set of 40 significant quantitative trait loci (QTL) representing 33 potential pheromone compounds. QTL clustered significantly on two chromosomes, chromosome 8 for genital compounds and chromosome 20 for wing compounds, and chromosome 20 was enriched for potential pheromone biosynthesis genes. There was minimal overlap between pheromone QTL and known QTL for mate choice and color pattern. Nonetheless, we did detect linkage between a QTL for wing androconial area and optix, a color pattern locus known to play a role in reproductive isolation in these species. This tight clustering of putative pheromone loci might contribute to coincident reproductive isolating barriers, facilitating speciation despite ongoing gene flow.