High-throughput Screening Techniques Research Articles

Machine Learning-Driven Discovery of Key Descriptors for CO2 Activation over Two-Dimensional Transition Metal Carbides and Nitrides.

Fusing high-throughput quantum mechanical screening techniques with modern artificial intelligence strategies is among the most fundamental ─yet revolutionary─ science activities, capable of opening new horizons in catalyst discovery. Here, we apply this strategy to the process of finding appropriate key descriptors for CO2 activation over two-dimensional transition metal (TM) carbides/nitrides (MXenes). Various machine learning (ML) models are developed to screen over 114 pure and defective MXenes, where the random forest regressor (RFR) ML scheme exhibits the best predictive performance for the CO2 adsorption energy, with a mean absolute error ± standard deviation of 0.16 ± 0.01 and 0.42 ± 0.06 eV for training and test data sets, respectively. Feature importance analysis revealed d-band center (εd), surface metal electronegativity (χM), and valence electron number of metal atoms (MV) as key descriptors for CO2 activation. These findings furnish a fundamental basis for designing novel MXene-based catalysts through the prediction of potential indicators for CO2 activation and their posterior usage.

Alzheimer's Disease: Significant Benefit from the Yeast-Based Models.

Alzheimer's disease (AD) is an age-related, multifaceted neurological disorder associated with accumulation of aggregated proteins (amyloid Aβ and hyperphosphorylated tau), loss of synapses and neurons, and alterations in microglia. AD was recognized by the World Health Organization as a global public health priority. The pursuit of a better understanding of AD forced researchers to pay attention to well-defined single-celled yeasts. Yeasts, despite obvious limitations in application to neuroscience, show high preservation of basic biological processes with all eukaryotic organisms and offer great advantages over other disease models due to the simplicity, high growth rates on low-cost substrates, relatively simple genetic manipulations, the large knowledge base and data collections, and availability of an unprecedented amount of genomic and proteomic toolboxes and high-throughput screening techniques, inaccessible to higher organisms. Research reviewed above clearly indicates that yeast models, together with other, more simple eukaryotic models including animal models, C. elegans and Drosophila, significantly contributed to understanding Aβ and tau biology. These models allowed high throughput screening of factors and drugs that interfere with Aβ oligomerization, aggregation and toxicity, and tau hyperphosphorylation. In the future, yeast models will remain relevant, with a focus on creating novel high throughput systems to facilitate the identification of the earliest AD biomarkers among different cellular networks in order to achieve the main goal-to develop new promising therapeutic strategies to treat or prevent the disease.

Screening of Ionic Liquid-Based Electrolytes for Al Dual-Ion Batteries: Thermodynamic Cycle and Combined MD-DFT Approaches

The electrochemical window (ECW) of the ionic liquid (IL) is the fundamental parameter to understand the stability of the electrolyte for a given electrode material. Here, we have considered room-temperature and molten salt-based 16 IL-based electrolytes, consisting of imidazolium-, pyrrolidinium-, urea-, and acetamide-based cations coupled with AlCl4 and OTf (trifluoromethanesulfonate) anions. Three different high-throughput computational screening techniques have been implemented to calculate the ECW of these electrolytes for dual-ion batteries (DIBs). The thermodynamic cycle method has been employed to calculate the oxidation and reduction potentials with respect to the Al3+/Al reference electrode for the individual ions of the IL. Classical molecular dynamics followed by DFT methods (MD + DFT) have been considered to calculate anodic and cathodic limiting potentials. From the MD + DFT, we have classified two methods, AIMD-min and AIMD-sp. The calculated ECWs for all considered ILs using the AIMD-min method are found to be close to experimental outcomes compared to other two methods. We show that the alkyl group of the imidazolium ring has a very limited effect on the ECW values. On the other hand, aromatic imidazolium rings are more prone to reduction compared to the nonaromatic pyrrolidinium ring. Moreover, we have investigated the contribution of cations and anions in cathodic and anodic potentials, from the study of density of states. Along with that, we have tested the robustness capability of the AIMD-min method and established a superior technique for other liquid systems to calculate the ECW. This work facilitates a step forward in selecting nonaqueous electrolytes for rechargeable DIBs.

A Fluorescence Polarization Assay for Macrodomains Facilitates the Identification of Potent Inhibitors of the SARS-CoV-2 Macrodomain.

Viral macrodomains, which can bind to and/or hydrolyze adenine diphosphate ribose (ADP-ribose or ADPr) from proteins, have been suggested to counteract host immune response and be viable targets for the development of antiviral drugs. Therefore, developing high-throughput screening (HTS) techniques for macrodomain inhibitors is of great interest. Herein, using a novel tracer TAMRA-ADPr, an ADP-ribose compound conjugated with tetramethylrhodamine, we developed a robust fluorescence polarization assay for various viral and human macrodomains including SARS-CoV-2 Macro1, VEEV Macro, CHIKV Macro, human MacroD1, MacroD2, and PARP9 Macro2. Using this assay, we validated Z8539 (IC50 6.4 μM) and GS441524 (IC50 15.2 μM), two literature-reported small-molecule inhibitors of SARS-CoV-2 Macro1. Our data suggest that GS441524 is highly selective for SARS-CoV-2 Macro1 over other human and viral macrodomains. Furthermore, using this assay, we identified pNP-ADPr (ADP-ribosylated p-nitrophenol, IC50 370 nM) and TFMU-ADPr (ADP-ribosylated trifluoromethyl umbelliferone, IC50 590 nM) as the most potent SARS-CoV-2 Macro1 binders reported to date. An X-ray crystal structure of SARS-CoV-2 Macro1 in complex with TFMU-ADPr revealed how the TFMU moiety contributes to the binding affinity. Our data demonstrate that this fluorescence polarization assay is a useful addition to the HTS methods for the identification of macrodomain inhibitors.

ISort enables automated complex microfluidic droplet sorting in an effort to democratize technology

Fluorescence-activated droplet sorting (FADS) is a widely used microfluidic technique for high-throughput screening. However, it requires highly trained specialists to determine optimal sorting parameters, and this results in a large combinatorial space that is challenging to optimize systematically. Additionally, it is currently challenging to track every single droplet within a screen, leading to compromised sorting and "hidden" false-positive events. To overcome these limitations, we have developed a setup in which the droplet frequency, spacing, and trajectory at the sorting junction are monitored in real time using impedance analysis. The resulting data are used to continuously optimize all parameters automatically and to counteract perturbations, resulting in higher throughput, higher reproducibility, increased robustness, and a beginner-friendly character. We believe this provides a missing piece for the spreading of phenotypic single-cell analysis methods, similar to what we have seen for single-cell genomics platforms.

Structure based High-Throughput Virtual Screening, Molecular Docking and Molecular Dynamics Study of anticancer natural compounds against fimbriae (FimA) protein of Porphyromonas gingivalis in oral squamous cell carcinoma.

Oral cancer is among the most common cancer in the world. Tobacco, alcohol, and viruses have been regarded as a well- known risk factors of OCC however, 15% of OSCC cases occurred each year without these known risk factors. Recently a myriad of studies has shown that bacterial infections lead to cancer. Accumulated shreds of evidence have demonstrated the role of Porphyromonas gingivalis (P. gingivalis) in OSCC. The virulence factor FimA ofP. gingivalisactivates the oncogenic pathways in OSCC by upregulating various cytokines. It also led to the inactivation of a tumor suppressor protein p53. The present Insilico study uses High-Throughput Virtual Screening, molecular docking, and molecular dynamics techniques to find the potential compounds against the target protein FimA. The goal of this study is to identify the anti-cancer lead compounds retrieved from natural sources that can be used to develop potent drug molecules to treat P.gingivalis-related OSCC. The anticancer natural compounds library was screened to identify the potential lead compounds. Furthermore, these lead compounds were subjected to precise docking, and based on the docking score potential lead compounds were identified. The top docked receptor-ligand complex was subjected to molecular dynamics simulation. A study of this insilico finding provides potent lead molecules which help in the development of therapeutic drugs against the target protein FimA in OSCC.

Combinatorial Material Strategy: Parallel Synthesis and High-Throughput Screening of WO3 Nanoplates Decorated with Noble Metals for VOCs Sensor

In this study, we report on the rapid preparation of WO3 nanoplates decorated with noble metals and evaluate their gas-sensing performance using a high-throughput screening technique. The incorporation of Pd significantly enhanced the gas-sensing properties, and, among all of the samples tested, the WO3 nanoplate containing 0.3 mol% Pd exhibited the highest response to 100 ppm xylene at 250 °C (Ra/Rg = 131.2), which was almost 56 times greater than that of the pure WO3 sample. Additionally, this sample demonstrated rapid response and recovery times (τresponse = 3.9 s and τrecovery = 189.2 s, respectively). The nanoplate samples were also classified and screened using cluster analysis, and the selected samples were optimized for use in a sensor array. By applying principal component analysis and Fisher discriminant analysis, four typical gases were identified and a potential sensitization mechanism was elucidated.

Predictive Nature of High-Throughput Assays in ADC Formulation Screening

Utilization of high-throughput biophysical screening techniques during early screening studies is warranted due to the limited amount of material and large number of samples. But the predictability of the data to longer-term storage stability is critical as the high-throughput methods assist in defining the design space for the longer-term studies. In this study, the biophysical properties of two ADCs in 16 formulation conditions were evaluated using high-throughput techniques. Conformational stability and colloidal stability were evaluated by determining Tm values, kD, B22, and Tagg. In addition, the samples were placed on stability and the extent of aggregate formation over the 8-week interval was determined. The rank order of the 16 different formulations in the high-throughput assays was compared to the rank order observed during the stability studies to assess the predictive capabilities of the screening methods. It was demonstrated that similar rank orders can be expected between high-throughput physical stability indicating assays such as Tagg and B22 and traditional aggregation by SEC data, whereas conformational stability read-outs (Tm) are less predictive. In addition, the high-throughput assays appropriately identified the poor performing formulation conditions, which is ultimately what is desired of screening assays.

High-Throughput Screening for the Potential Inhibitors of SARS-CoV-2 with Essential Dynamic Behavior.

Global health security has been challenged by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. Due to the lengthy process of generating vaccinations, it is vital to reposition currently available drugs in order to relieve anti-epidemic tensions and accelerate the development of therapies for Coronavirus Disease 2019 (COVID-19), the public threat caused by SARS-CoV-2. High throughput screening techniques have established their roles in the evaluation of already available medications and the search for novel potential agents with desirable chemical space and more cost-effectiveness. Here, we present the architectural aspects of highthroughput screening for SARS-CoV-2 inhibitors, especially three generations of virtual screening methodologies with structural dynamics: ligand-based screening, receptor-based screening, and machine learning (ML)-based scoring functions (SFs). By outlining the benefits and drawbacks, we hope that researchers will be motivated to adopt these methods in the development of novel anti- SARS-CoV-2 agents.

Inhibiting the transcription and replication of Ebola viruses by disrupting the nucleoprotein and VP30 protein interaction with small molecules.

Ebola virus (EBOV) causes hemorrhagic fever in humans with high morbidity and fatality. Although over 45 years have passed since the first EBOV outbreak, small molecule drugs are not yet available. Ebola viral protein VP30 is a unique RNA synthesis cofactor, and the VP30/NP interaction plays a critical role in initiating the transcription and propagation of EBOV. Here, we designed a high-throughput screening technique based on a competitive binding assay to bind VP30 between an NP-derived peptide and a chemical compound. By screening a library of 8004 compounds, we obtained two lead compounds, Embelin and Kobe2602. The binding of these compounds to the VP30-NP interface was validated by dose-dependent competitive binding assay, surface plasmon resonance, and thermal shift assay. Moreover, the compounds were confirmed to inhibit the transcription and replication of the Ebola genome by a minigenome assay. Similar results were obtained for their two respective analogs (8-gingerol and Kobe0065). Interestingly, these two structurally different molecules exhibit synergistic binding to the VP30/NP interface. The antiviral efficacy (EC50) increased from 1 μM by Kobe0065 alone to 351 nM when Kobe0065 and Embelin were combined in a 4:1 ratio. The synergistic anti-EBOV effect provides a strong incentive for further developing these lead compounds in future studies.

Scaffold identification and drug repurposing for finding potential Dengue envelope inhibitors through ligand-based pharmacophore model

Most of the existing DENV entry inhibitors were discovered through structure-based, high-throughput screening techniques and optimization approaches by aiming β-OG pocket. However, the class of precise chemical scaffolds with superior antiviral activity targeting the early stages of virus infection that is considered to be beneficial in therapeutics and is still in process. In this study, ligand-based pharmacophore modeling using existing DENV entry inhibitors provided two best models, AADRR-2 and AAADR-2 (A- accepter, D- donor, R-ring) to screen public and DrugBank datasets. Further, approximately 36000 molecules were filtered using Zinc13 by employing the ideal validated models. Additionally, using β-OG binding pocket as target site, molecular docking experiments including induced-fit studies were conducted that provided further structurally divergent ligands. Moreover, the refined list of preferential hits were filtered out based on the best fitness score, binding energy and interaction paradigm, among them fused pyrimidine, hydrazone and biphenyl core comprising scaffolds were identified possessing profound interaction profile with key amino acid residues, ALA-50, GLN-200, PHE-193 and PHE-279 in 100 ns MD simulations. Additionally, the search for similar chemical fingerprints from DrugBank library was also carried out and Eltrombopag (Promacta/Revolade® prescribed in thrombocytopenia) was identified as a preferential β-OG pocket binder. The identified pyrazole-based hydrazone class of drug, Eltrombopag is in phase II clinical trials employed to treat dengue-mediated thrombocytopenia. Communicated by Ramaswamy H. Sarma

Functional Peptides from One-bead One-compound High-throughput Screening Technique

Functional Peptides from One-bead One-compound High-throughput Screening Technique

Ecotoxicological Effects of the Anionic Surfactant Sodium Dodecyl Sulfate (SDS) in Two Marine Primary Producers: Phaeodactylum tricornutum and Ulva lactuca.

Sodium Dodecyl Sulfate (SDS) is an anionic surfactant, extensively used in detergents, household and personal care products, as well as in industrial processes. The present study aimed to disclose the potential toxicological effects of SDS exposure under environmentally relevant concentrations (0, 0.1, 1, 3, and 10 mg L-1) on the physiology and biochemistry (photosynthesis, pigment, and lipid composition, antioxidative systems, and energy balance) of two marine autotrophs: the diatom Phaeodactylum tricornutum and the macroalgae Ulva lactuca. A growth rate (GR) reduction in P. tricornutum was observed with a classic dose-response effect towards the highest applied concentration, while a GR increase occurred in U. lactuca. Regarding photochemistry, the decrease in the fluorescence of the OJIP curves and laser-induced fluorescence allowed a better separation between SDS treatments in U. lactuca compared with P. tricornutum. Although all pigments significantly decreased in U. lactuca at the highest concentrations (except for antheraxanthin), no significant variations occurred in P. tricornutum. On the other hand, changes in fatty acid content were observed in P. tricornutum but not in U. lactuca. In terms of classical biomarker assessment, a dose-effect relationship of individual biomarkers versus SDS dose applied; U. lactuca displayed a higher number of biomarker candidates, including those in distinct metabolic pathways, increasing its usefulness for ecotoxicological applications. By evaluating the potential application of optical and biochemical traits, it was evident that the fatty acid profiles of the different exposure groups are excellent candidates in P. tricornutum, concomitant with the characteristics of this anionic surfactant. On the other hand, the results presented by laser-induced fluorescence and some parameters of PAM fluorometry in U. lactuca may be an advantage in the field, offering non-invasive, fast, easy-to-use, high-throughput screening techniques as excellent tools for ecotoxicology assessment.

T Cell-Mediated Tumor Killing-Related Classification of the Immune Microenvironment and Prognosis Prediction of Lung Adenocarcinoma.

Although immune checkpoint inhibitors (ICI) are a promising therapeutic strategy for lung adenocarcinoma (LUAD), individual subgroups that might benefit from them are yet to be identified. As T cell-mediated tumor killing (TTK) is an underlying mechanism of ICI, we identified subtypes based on genes associated with TTK sensitivity and assessed their predictive significance for LUAD immunotherapies. Using high-throughput screening techniques, genes regulating the sensitivity of T cell-mediated tumor killing (GSTTK) with differential expression and associations with prognosis were discovered in LUAD. Furthermore, patients with LUAD were divided into subgroups using unsupervised clustering based on GSTTK. Significant differences were observed in the tumor immune microenvironment (TIME), genetic mutation and immunotherapy response across subgroups. Finally, the prognostic significance of a scoring algorithm based on GSTTK was assessed. A total of 6 out of 641 GSTTK exhibited differential expression in LUAD and were associated with prognosis. Patients were grouped into two categories based on the expression of the six GSTTK, which represented different TTK immune microenvironments in LUAD. Immune cell infiltration, survival difference, somatic mutation, functional enrichment and immunotherapy responses also varied between the two categories. Additionally, a scoring algorithm accurately distinguished overall survival rates across populations. TTK had a crucial influence on the development of the varying TIME. Evaluation of the varied TTK modes of different tumors enhanced our understanding of TIME characteristics, wherein the changes in T cell activity in LUAD are reflected. Thus, this study guides the development of more effective therapeutic methods.

A high-throughput approach to developing and optimizing mixed-mode membrane chromatography for protein purification.

Membrane chromatography has been established as a viable alternative to packed-bed column chromatography for the purification of therapeutic proteins. Purification via membrane chromatography offers key advantages, including higher productivity and reduced buffer usage. Unlike column chromatography purification, the utilization of high-throughput screening in order to reduce development times and material requirements has been a challenge for membrane chromatography. This research focused on the development of a new, high-throughput screening technique for use in screening membrane chromatography conditions for monoclonal antibody purification. The developed screen utilizes a 96-well plate format, thereby allowing for the screening of multiple different membrane conditions at once. For this study, four mixed-mode cation exchange membranes and one cation exchange membrane were evaluated on the plate. The screen is performed in a similar manner to that of a resin slurry plate screen, however, instead of a single loading step, the antibody feed was loaded in 50 mg/ml increments up to a maximum loading of 450 mg/ml. Performing a similar, incremental loading on a resin plate would be impractical, as mixing times are substantially longer due to pore diffusion limitations. However, due to the significantly faster rate of mass transfer for membranes relative to resin, mixing times could be reduced by up to a factor of sixty on the membrane plate. Additional optimization showed that higher hydrophobicity can potentially lead to slower kinetics and mixing times that may need to be adjusted accordingly. The end result is a screen that has been proven to provide results comparable to those obtained on larger-scale membrane purification runs while also enabling exploration of a much greater operating space and significantly reducing the feed materials required.

Review on In Silico Methods, High-throughput Screening Techniques, and Cell Culture Based In Vitro Assays for SARS-CoV-2.

The COVID-19 outbreak caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to have high incidence and mortality rate globally. To meet the increasingly growing demand for new therapeutic drugs and vaccines, researchers are developing different diagnostic techniques focused on screening new drugs in clinical use, developing an antibody targeting a SARS-CoV-2 receptor, or interrupting infection/replication mechanisms of SARS-CoV-2. Although many prestigious research publications are addressing this subject, there is no open access platform where all experimental techniques for COVID-19 research can be seen as a whole. Many researchers have accelerated the development of in silico methods, high-throughput screening techniques, and in vitro assays. This development has played an important role in the emergence of improved, innovative strategies, including different antiviral drug development, new drug discovery protocols, combinations of approved drugs, and setting up new drug classes during the COVID-19 outbreak. Hence, the present review discusses the current literature on these modalities, including virtual in silico methods for instant ligand- and target-driven based techniques, nucleic acid amplification tests, and in vitro models based on sensitive cell cultures, tissue equivalents, organoids, and SARS-CoV-2 neutralization systems (lentiviral pseudotype, viral isolates, etc.). This pack of complementary tests informs researchers about the accurate, most relevant emerging techniques available and in vitro assays allow them to understand their strengths and limitations. This review could be a pioneer reference guide for the development of logical algorithmic approaches for new drugs and vaccine strategies against COVID-19.

Investigation of Photocatalyst Composites for Pollutant Degradation in a Microslit Reactor Utilizing High Throughput Screening Techniques.

The high-throughput screening investigations on TiO2 based photocatalyst composites presented here have been carried out in a 60-fold parallel photoreactor. Additional catalyst testing was performed in a microslit reactor system with immobilized catalysts. For further enhancing the photocatalytic activity of TiO2 (P25), composites of P25 and, for example, Bi2 O3 , CeO2 , g-C3 N4 , WO3 or ZnO were formulated in different nominal molar ratios. The catalysts' performances were assessed by their conversion of 17α-ethinyl estradiol (EE2) in aqueous solutions, determined by LC-MS. Findings show rapid EE2 conversions in short residence times. The extensive testing of catalysts led to the conclusion that the photocatalytic conversion is rather a function of residence time than a function of the materials utilized. This makes adequate process development seem more important than material development. The novelty of this contribution lies in the unique combination of testing a wide range of composite catalysts in a unique microreactor geometry.

Insilco and Invitro approaches identify novel dual PI3K/AKT pathway inhibitors to control acute myeloid leukemia cell proliferations.

Acute myeloid leukemia (AML) is characterized by disruption of intracellular signaling due to aberration of extracellular signaling pathways, namely PI3K/AKT cascade, by dysregulating erythropoiesis and myelopoiesis. Therefore, inhibition of PI3K/AKT, either individually, or by dual inhibitors, is shown to be effective in suppression of tumorigenesis. To increase the therapeutic viability and decrease adverse effects, including cytotoxicity due to off-target kinase inhibitions, customized targeted pharmacological agents are needed that would have greater treatment potential. In this work, using an interdisciplinary approach, we have identified dual inhibitors targeted to PI3K and AKT to significantly repress the cell proliferation in AML cancers. Diversity-based high-throughput virtual screening (D-HTVS) technique followed by conventional docking approach identified small molecules from ChemBridge library, having high binding affinity for PI3KCG subunit. Further computational screening of top identified PI3K-specific lead molecules predicts dual inhibitors with high binding affinity for AKT. To rule out the possibility for cross-reaction/off-target effects of identified small molecules, lead compounds having nil or negligible binding to PI3KCA- and PI3KCB subunits were chosen. Computational screening, enzyme inhibition and cell proliferation assays show compound C16,5-{[(1,3-dioxo-1H-benzo[de]isoquinolin-2(3H)-yl)amino]methylene}-1-phenyl-2,4,6(1H,3H,5H)-pyrimidinetrione has better affinity for PI3KCG, delta, and AKT kinases compared to their respective known/established inhibitors, and has significant anti-cell proliferation activity in AML cells with a GI50 values of 77.25nM and 49.65nM in THP-1 and HL-60 cells, respectively. This work proposes a novel dual inhibitor that selectively targets PI3K/AKT and suppresses cell proliferation in AML cells as a potential lead molecule for treating AML cancers.

Detecting zoonotic and non-zoonotic pathogens in livestock and their ticks in Corsican wetlands.

Corsica is a large French island in the Mediterranean Sea with high human and animal migration rates, especially near wetlands where these migrations are particularly frequent. Among the livestock populations, cattle and sheep are widely present all across the entire Mediterranean region. Trade can be responsible for the circulation of numerous pathogens and their vectors, thereby representing a health and economic threat for the livestock industry. The objective of our study was to investigate the presence of pathogens in cattle and sheep farms in the wetlands of Corsica using a high-throughput screening technique. In our study, blood samples and ticks were collected from cattle and sheep in 20 municipalities near Corsican wetlands to screen for the presence of various types of pathogens. The samples were processed using a high-throughput screening technique based on real-time microfluidic PCR: 45 pathogens were screened in 47 samples simultaneously. A total of 372 cattle and 74 sheep were sampled, and 444 ticks were collected from cattle. Out of the eight tick species detected, the main one was Rhipicephalus bursa (38.7% of the ticks collected). From cattle blood samples, one species and two genera were found: Anaplasma marginale, Trypanosoma sp. and Babesia sp. in respectively 61.5%, 58.3% and 12.2% of the cattle blood samples. From sheep blood samples, 74.3% were positive for Anaplasma sp, 2.7% for Anaplasma ovis and 1.4% for Anaplasma capra. This is the first report of A. ovis DNA in blood samples from sheep in Corsica. Out of 444 the tick samples, 114 were positive: 77.2% for Rickettsia aeschlimannii, 20.2% for Rickettsia sp., 3.5% for Babesia sp. and 1.8% for Anaplasma sp. Among them, 2.7% were co-infected with R. aeschlimannii and Babesia sp. Our results confirm the extent of possible circulation of different pathogens near Corsican wetlands, not only in ticks collected from livestock but also directly in cattle and sheep, with two (Trypanosoma sp. and Babesia sp.) being detected for the first time in cattle, one for the first time in sheep (A. ovis) and one for the first time in Corsica (A. capra).

Marine Bioprospecting, Biocatalysis and Process Development.

Oceans possess tremendous diversity in microbial life. The enzymatic machinery that marine bacteria present is the result of extensive evolution to assist cell survival under the harsh and continuously changing conditions found in the marine environment. Several bacterial cells and enzymes are already used at an industrial scale, but novel biocatalysts are still needed for sustainable industrial applications, with benefits for both public health and the environment. Metagenomic techniques have enabled the discovery of novel biocatalysts, biosynthetic pathways, and microbial identification without their cultivation. However, a key stage for application of novel biocatalysts is the need for rapid evaluation of the feasibility of the bioprocess. Cultivation of not-yet-cultured bacteria is challenging and requires new methodologies to enable growth of the bacteria present in collected environmental samples, but, once a bacterium is isolated, its enzyme activities are easily measured. High-throughput screening techniques have also been used successfully, and innovative in vitro screening platforms to rapidly identify relevant enzymatic activities continue to improve. Small-scale approaches and process integration could improve the study and development of new bioprocesses to produce commercially interesting products. In this work, the latest studies related to (i) the growth of marine bacteria under laboratorial conditions, (ii) screening techniques for bioprospecting, and (iii) bioprocess development using microreactors and miniaturized systems are reviewed and discussed.