Wet-lab Data Research Articles

Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning

Accurately modeling the protein fitness landscapes holds great importance for protein engineering. Pre-trained protein language models have achieved state-of-the-art performance in predicting protein fitness without wet-lab experimental data, but their accuracy and interpretability remain limited. On the other hand, traditional supervised deep learning models require abundant labeled training examples for performance improvements, posing a practical barrier. In this work, we introduce FSFP, a training strategy that can effectively optimize protein language models under extreme data scarcity for fitness prediction. By combining meta-transfer learning, learning to rank, and parameter-efficient fine-tuning, FSFP can significantly boost the performance of various protein language models using merely tens of labeled single-site mutants from the target protein. In silico benchmarks across 87 deep mutational scanning datasets demonstrate FSFP’s superiority over both unsupervised and supervised baselines. Furthermore, we successfully apply FSFP to engineer the Phi29 DNA polymerase through wet-lab experiments, achieving a 25% increase in the positive rate. These results underscore the potential of our approach in aiding AI-guided protein engineering.

Subjective data models in bioinformatics and how wet lab and computational biologists conceptualise data

Biological science produces “big data” in varied formats, which necessitates using computational tools to process, integrate, and analyse data. Researchers using computational biology tools range from those using computers for communication, to those writing analysis code. We examine differences in how researchers conceptualise the same data, which we call “subjective data models”. We interviewed 22 people with biological experience and varied levels of computational experience, and found that many had fluid subjective data models that changed depending on circumstance. Surprisingly, results did not cluster around participants’ computational experience levels. People did not consistently map entities from abstract data models to the real-world entities in files, and certain data identifier formats were easier to infer meaning from than others. Real-world implications: 1) software engineers should design interfaces for task performance, emulating popular user interfaces, rather than targeting professional backgrounds; 2) when insufficient context is provided, people may guess what data means, whether or not they are correct, emphasising the importance of contextual metadata to remove the need for erroneous guesswork.

Elucidating the Structural Impacts of Protein InDels.

The effects of amino acid insertions and deletions (InDels) remain a rather under-explored area of structural biology. These variations oftentimes are the cause of numerous disease phenotypes. In spite of this, research to study InDels and their structural significance remains limited, primarily due to a lack of experimental information and computational methods. In this work, we fill this gap by modeling InDels computationally; we investigate the rigidity differences between the wildtype and a mutant variant with one or more InDels. Further, we compare how structural effects due to InDels differ from the effects of amino acid substitutions, which are another type of amino acid mutation. We finish by performing a correlation analysis between our rigidity-based metrics and wet lab data for their ability to infer the effects of InDels on protein fitness.

TGR5 Agonist INT-777 Alleviates Inflammatory Neurodegeneration in Parkinson’s Disease Mouse Model by Modulating Mitochondrial Dynamics in Microglia

Parkinson’s disease (PD) is one of the most common chronic progressive neurodegenerative diseases that affects both motor and non-motor functions. Bile acids modulate the immune system by targeting brain receptors. INT-777, a 6α-ethyl-23(S)-methyl derivative of cholic acid (S-EMCA), acts as an agonist for Takeda G protein-coupled receptor-5 (TGR5) and has neuroprotective properties. However, the effects of INT-777 on PD have not yet been investigated. In a subchronic PD model, mice treated with 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) developed motor deficits and cognitive impairment that were ameliorated after intranasal administration of INT-777. INT-777 prevented MPTP-induced neurodegeneration and microglia activation in the substantia nigra pars compacta, hippocampus, and cortical layer V. Based on bioinformatics and wet lab data, INT-777 inhibited microglia activation by suppressing the release of tumor necrosis factor alpha (TNF-α) in the hippocampus, along with secondary chemokines (C–C motif ligand 3 (CCL3) and CCL6) in these three brain regions. INT-777 inhibited TNF-α production by repairing mitochondrial damage, which was associated with nuclear factor-erythroid 2-related factor-2 (NRF2) activation and p62/LC3B-mediated autophagy. INT-777 reversed the downregulation of heme oxygenase-1 (HO1), NAD(P)H quinone oxidoreductase-1 (NQO1) and accumulation of p62 in microglia treated with 1-methyl-4-phenylpyridinium (MPP+). However, TGR5 knockdown in microglia abolished INT-777′s inhibition of TNF-α release, resulting in neuronal death. Therefore, PD cognitive impairment is associated with hippocampal TNF-α elevation as a result of mitochondrial damage in microglia. Our data reveal the potential role of TGR5 in modulating inflammation-mediated neurodegeneration in PD, and provides new insights for bile acid metabolites as promising disease-modifying drugs for PD.

Association of HLA gene polymorphism with susceptibility, severity, and mortality of COVID-19: A systematic review.

HLA is crucial for appropriate immune responses in several viral infections, as well as in severe acute respiratory syndrome coronavirus-2 (SARS CoV-2). The unpredictable nature of Coronavirus Disease 19 (COVID-19), observed in both inter-individual and inter-population level, raises the question, to what extent the HLA, as part of host genetic factors, contribute to disease susceptibility and prognosis. We aimed to identify significant HLAs, those were investigated till now, for their association with COVID-19. Three databases were searched (PubMed, Cochrane library, and Web of Science) and articles published between January 2020 and May 2021 were included for in-depth analysis. Two separate teams including four observers independently extracted the summary data, with discrepancies resolved by consensus. This study is registered with PROSPERO (CRD42021251670). Of 1278 studies identified, 36 articles were included consisting of 794,571 participants. Countries from the European region appeared in the highest number of studies and vice versa for countries from South East Asia. Among 117 significantly altered alleles, 85 (72.65%) were found to have a positive correlation with COVID-19 and 33 (27.35%) alleles were observed having a negative correlation. HLA A*02 is the most investigated allele (n=18) and showed contradictory results. Non-classical HLA E was explored by only one study and it showed that E*01:01 is associated with severity. Both in silico and wet lab data were considered and contrasting results were found from two approaches. Although several HLAs depicted significant association, nothing conclusive could be drawn because of heterogeneity in study designs, HLA typing methods, and so forth. This systematic review shows that, though HLAs play role in COVID-19 susceptibility, severity, and mortality, more uniformly designed, interrelated studies with the inclusion of global data, for use in evidence-based medicine are needed.

Clinical lipidomics – A community-driven roadmap to translate research into clinical applications

Lipid metabolites, beyond triglycerides and cholesterol, have been shown to have vast potential for applications in clinical applications, with substantial societal and economical value. To successfully evolve from the current research-grade methods to assays suitable for routine clinical applications, a harmonization – if not standardization – of these mass spectrometry-based workflows is necessary. Input on clinical needs and technological capabilities must be obtained from all relevant stakeholders, including wet lab scientists, informaticians and data scientists, manufacturers, and medical professionals. In order to build bridges between this diverse group of professionals, the International Lipidomics Society and its Clinical Lipidomics Interest Group were created. This opinion article is intended to provide an overview of international efforts to tackle the issues of workflow harmonization, and to serve as an open invitation for others to join this growing community.

Relating simulation studies by provenance—Developing a family of Wnt signaling models

For many biological systems, a variety of simulation models exist. A new simulation model is rarely developed from scratch, but rather revises and extends an existing one. A key challenge, however, is to decide which model might be an appropriate starting point for a particular problem and why. To answer this question, we need to identify entities and activities that contributed to the development of a simulation model. Therefore, we exploit the provenance data model, PROV-DM, of the World Wide Web Consortium and, building on previous work, continue developing a PROV ontology for simulation studies. Based on a case study of 19 Wnt/β-catenin signaling models, we identify crucial entities and activities as well as useful metadata to both capture the provenance information from individual simulation studies and relate these forming a family of models. The approach is implemented in WebProv, a web application for inserting and querying provenance information. Our specialization of PROV-DM contains the entities Research Question, Assumption, Requirement, Qualitative Model, Simulation Model, Simulation Experiment, Simulation Data, and Wet-lab Data as well as activities referring to building, calibrating, validating, and analyzing a simulation model. We show that most Wnt simulation models are connected to other Wnt models by using (parts of) these models. However, the overlap, especially regarding the Wet-lab Data used for calibration or validation of the models is small. Making these aspects of developing a model explicit and queryable is an important step for assessing and reusing simulation models more effectively. Exposing this information helps to integrate a new simulation model within a family of existing ones and may lead to the development of more robust and valid simulation models. We hope that our approach becomes part of a standardization effort and that modelers adopt the benefits of provenance when considering or creating simulation models.

Quantitative Molecular Models for Biological Processes: Modeling of Signal Transduction Networks with ANIMO.

Computational modeling of biological networks is increasing in popularity due to the increased demand for understanding biological processes. This understanding requires integration of a variety of experimental data that allows understanding of complex mechanisms regulating cell and tissue function. However, the mathematical complexity of many modeling tools have thusfar prevented broad adaptation and effective use by molecular biologists. In this chapter, we show by example how one can start building a model in ANIMO and how to adapt the model to experimental data. We show how this model can be used for simulating network activities, testing hypotheses, and how to improve the model using wet-lab data.

Viral pandemic preparedness: A pluripotent stem cell-based machine-learning platform for simulating SARS-CoV-2 infection to enable drug discovery and repurposing.

Infection with the SARS‐CoV‐2 virus has rapidly become a global pandemic for which we were not prepared. Several clinical trials using previously approved drugs and drug combinations are urgently under way to improve the current situation. A vaccine option has only recently become available, but worldwide distribution is still a challenge. It is imperative that, for future viral pandemic preparedness, we have a rapid screening technology for drug discovery and repurposing. The primary purpose of this research project was to evaluate the DeepNEU stem‐cell based platform by creating and validating computer simulations of artificial lung cells infected with SARS‐CoV‐2 to enable the rapid identification of antiviral therapeutic targets and drug repurposing. The data generated from this project indicate that (a) human alveolar type lung cells can be simulated by DeepNEU (v5.0), (b) these simulated cells can then be infected with simulated SARS‐CoV‐2 virus, (c) the unsupervised learning system performed well in all simulations based on available published wet lab data, and (d) the platform identified potentially effective anti‐SARS‐CoV2 combinations of known drugs for urgent clinical study. The data also suggest that DeepNEU can identify potential therapeutic targets for expedited vaccine development. We conclude that based on published data plus current DeepNEU results, continued development of the DeepNEU platform will improve our preparedness for and response to future viral outbreaks. This can be achieved through rapid identification of potential therapeutic options for clinical testing as soon as the viral genome has been confirmed.

Amygdalin based G-6-P synthase inhibitors as novel preservatives for food and pharmaceutical products

G-6-P synthase enzyme has been involved in the synthesis of the microbial cell wall, and its inhibition may lead to the antimicrobial effect. In the present study, we designed a library of amygdalin derivatives, and two most active derivatives selected on the basis of various parameters viz. dock score, binding energy, and ADMET data using molecular docking software (Schrodinger’s Maestro). The selected derivatives were synthesized and evaluated for their antioxidant and antimicrobial potential against several Gram (+ ve), Gram (−ve), as well as fungal strains. The results indicated that synthesized compounds exhibited good antioxidant, antimicrobial, and better preservative efficacy in food preparation as compared to the standard compounds. No significant differences were observed in different parameters as confirmed by Kruskal–Wallis test (p < 0.05). Docking results have been found in good correlation with experimental wet-lab data. Moreover, the mechanistic insight into the docking poses has also been explored by binding interactions of amygdalin derivative inside the dynamic site of G-6-P synthase.

In silico screening for the interaction of small molecules with their targets and evaluation of therapeutic efficacy by free online tools

Pharmaceutical chemistry deals with the process of isolating organic compounds from natural sources or chemically synthesizing them in order to explore potential drugs. Drugs are small molecules, used to prevent or treat various diseases. Of several lead molecules, only few of them reach clinical trial phases and emerge as effective drugs, whereas the majority will be eliminated at different stages. On the other hand, due to the lack of proper identification of their pharmacokinetic properties and biological potential, many small molecules fail to reach this stage. This could be because of the fact that it is either time consuming and costly or there is full of uncertainty due to lack of analyses that are necessary for the confirmation. In the post-genomic era, computational methods have been implemented in almost all stages of drug research and development owing to the drastic increase in the available knowledge about small molecules and the target biomacromolecule. This includes identifying the suitable and specific targets for drug candidates, lead discovery, lead optimization and ultimately preclinical phases. In this context, numerous websites have become highly valuable and influence the drug development and discovery process. Here, we have attempted to bring together some of the online computational approaches and tools that are available to facilitate research efforts in the field of drug discovery and drug design. The output information from these tools is extremely helpful in selecting and deciding about the future direction or specific path needed to be followed by the researchers. These computational methods are indeed help to focus the intended research in the right direction. As detailed in this review, the information provided about the servers and methods should be useful throughout the process of screening of synthesized or chemical database originated small molecules to find the appropriate targets along with the active sites without depending on any commercial tools or time-consuming and costly assays. It should however be remembered that the bioinformatics-based prediction cannot completely replace the wet lab data of chemical compounds or specific assays.

Function2Form Bridge-Toward synthetic protein holistic performance prediction.

Protein engineering and synthetic biology stand to benefit immensely from recent advances in silico tools for structural and functional analyses of proteins. In the context of designing novel proteins, current in silico tools inform the user on individual parameters of a query protein, with output scores/metrics unique to each parameter. In reality, proteins feature multiple "parts"/functions and modification of a protein aimed at altering a given part, typically has collateral impact on other protein parts. A system for prediction of the combined effect of design parameters on the overall performance of the final protein does not exist. Function2Form Bridge (F2F-Bridge) attempts to address this by combining the scores of different design parameters pertaining to the protein being analyzed into a single easily interpreted output describing overall performance. The strategy comprises of (a) a mathematical strategy combining data from a myriad of in silico tools into an OP-score (a singular score informing on a user-defined overall performance) and (b) the F2F Plot, a graphical means of informing the wetlab biologist holistically on designed construct suitability in the context of multiple parameters, highlighting scope for improvement. F2F predictive output was compared with wetlab data from a range of synthetic proteins designed, built, and tested for this study. Statistical/machine learning approaches for predicting overall performance, for use alongside the F2F plot, were also examined. Comparisons between wetlab performance and F2F predictions demonstrated close and reliable correlations. This user-friendly strategy represents a pivotal enabler in increasing the accessibility of synthetic protein building and de novo protein design.

Assessing the comparability of different DNA extraction and amplification methods in gut microbial community profiling.

Automated, high-throughput technologies are becoming increasingly common in microbiome studies to decrease costs and increase efficiency. However, in microbiome studies, small differences in methodology – including storage conditions, wet lab methods, sequencing platforms and data analysis – can influence the reproducibility and comparability of data across studies. There has been limited testing of the effects of high-throughput methods, including microfluidic PCR technologies. In this paper, we compare two extraction methods (the QIAamp DNA Stool Mini Kit and the MoBio PowerSoil DNA Isolation kit), two taq polymerase enzymes (MyTaq HS Red Mix and Accustart II PCR ToughMix), two primer sets (V3–V4 and V4–V5) and two amplification methods (a common two-step PCR protocol and amplicon library preparation on the Fluidigm Access Array system that allows automated multiplexing of primers). Gut microbial community profiles were significantly affected by all variables. While there were no significant differences in alpha diversity measured between the two extraction methods, there was an effect of extraction method on community composition measured by unweighted UniFrac distances. Both amplification method and primers had a significant effect on both alpha diversity and community composition. The relative abundance of Actinobacteria was significantly lower when using the MoBio kit or Fluidigm amplification method, and the relative abundance of Firmicutes was lower when using the Qiagen kit. Microbial community profiles based on Fluidigm-generated amplicon libraries were not comparable to those generated with more commonly used methods. Researchers should carefully consider the limitations and biases that different extraction and amplification methods can introduce into their results. Additionally, more thorough benchmarking of automated and multiplexing methods is necessary to determine the magnitude of the potential trade-off between the quality and the quantity of data.

Genome analysis of antimicrobial resistance, virulence, and plasmid presence in Turkish Salmonella serovar Infantis isolates

Salmonella enterica subsp. enterica serovar Infantis (S. Infantis) isolates were found to have a multi-drug resistance profile (kanamycin, streptomycin, nalidixic acid, tetracycline, sulfonamide, and sometimes to ampicillin) and high prevalence (91%) in Turkish poultry in our previous studies. To investigate the mechanism behind multi-drug antimicrobial resistance (AMR) and high prevalence in Turkish poultry, 23 of the isolates were sequenced for comparative genomic analyses including: SNP-based comparison to S. Infantis from other countries, comparison of antimicrobial resistance genes (AMGs) with AMR phenotypes, and plasmid identification and annotation. Whole-genome SNP-based phylogenetic analysis found that all 23 Turkish S. Infantis isolates formed a distinct, well-supported clade, separate from 243 comparison S. Infantis genomes in GenomeTrakr identified as from the US and EU; the isolates most closely related to the cluster of these Turkish isolates were from Israel and Egypt. AMGs identified by bioinformatic analysis, without differentiating chromosomal or plasmid located genes, implied AMR phenotypes with 94% similarity overall to wet lab data, which was performed by phenotypic and conventional PCR methods. Most of the S. Infantis (21/23) isolates had identifiable plasmids, with 76% (16/21) larger than 100 kb and 48% (10/21) larger than 200 kb. A plasmid larger than 200 kb, with the incompatibility type of IncX1, similar to United States S. Infantis plasmid N55391 (99% query coverage and 99% identity overall), which itself is similar to Italian and Hungarian S. Infantis plasmids. Turkish S. Infantis plasmids had different beta-lactam resistance genes (blaTEM-70, blaTEM-148 and blaTEM-198) than the gene blaCTX-M-65 found in S. Infantis plasmids from other countries. This is the first observation of these three genes in S. Infantis isolates. The plasmids larger than 200 kb had two distinct regions of interest: Site 1 and Site 2. Site 1 (around 130 kb) had virulence- and bacteriocin- associated genes such as bacteriocin secretion system and type II toxin-antitoxin system genes (vagC, ccdA, ccdB, mchE, cvaB) and an aminoglycoside resistance gene (str). Site 2 (around 75–110 kb) had the antimicrobial resistance genes (aadA, sulI, tetA, tetR) and mercury (mer) resistance gene on tranposons Tn552 and Tn501. Presence of these AMR and virulence genes suggests they may have a role in the emergence of S. Infantis in poultry and support treating this serotype as a an important human health hazard.

Abstract 3128: miR-9 expression regulates and predicts the response to EGFR inhibitors in head &amp; neck squamous cell carcinoma

Abstract Introduction. Most Head &amp; Neck Squamous Cell Carcinoma (HNSCC) patients are diagnosed with a locally advanced disease. Radiotherapy (RT) plus anti-EGFR monoclonal antibodies (Cetuximab - CTX) represents an effective combination therapy for locally advanced HNSCC patients. However, the 5-year overall survival is still 45%, mainly due to the appearance of loco-regional recurrences, suggesting that the identification and validation of predictive biomarkers of CTX activity is urgently needed to identify patients at high-risk of recurrence, who will benefit more from this therapeutic strategy. We recently validated a 4-microRNAs (miRs) signature, related to Epithelial to Mesenchymal Transition (EMT) and able to stratify HNSCC patients at high-risk of recurrence development. Among these 4 miRs, miR-9 was the only up-regulated in primary tumors from patients who developed recurrence within 2-year follow-up. Here we evaluated whether miR-9 expression had a functional role in HNSCC onset, progression and response to therapies. Methods. miR-9 modified (overexpressing and silenced) HNSCC cells were generated, characterized for their growth and response to radio-, chemo- and targeted-therapies (i.e.Cisplatin, Paclitaxel, 5-Fluorouracil and CTX) in vitro and in vivo. Preclinical evidences were confirmed in a cohort of primary HNSCC samples by intercrossing the expression of miR-9 and selected target genes with patients’ clinical variables and response to therapy. Results. Biochemical and biological in vitro and in vivo experiments showed that high miR-9 expression triggers EMT and increases tumor initiating properties in HNSCC cells. Interestingly, miR-9 silenced HNSCC cells displayed a higher sensitivity to RT and CTX, but not to chemotherapy, when compared to controls. Using an in vivo model of HNSCC, we observed that intra-tumor injection of anti-miR-9 improves the efficacy of RT alone and remarkably in combination with CTX. Mechanistically, we demonstrated that in HNSCC cells, EGFR activation triggers miR-9 expression that promotes the transcription of SP1, establishing a positive forward loop between EGFR activation and SP1 transcription. Accordingly, in primary HNSCC miR-9 expression strongly correlates with the one of SP1 and EGFR. More importantly, in a cohort of HNSCC patients treated with RT plus CTX, high miR-9 expression acts as an efficient predictive biomarker of intrinsic resistance. Conclusion. Altogether by integrating wet-lab and clinical data, we provide strong evidences indicating that in HNSCC a subpopulation of miR-9 expressing cells is intrinsically resistant to RT plus CTX therapy, confirming its potential prognostic value. Since we set up an assay to easily and quantitatively evaluate miR-9 expression in diagnostic tumor biopsies, we propose that it could be used to personalize the treatments for this group of patients, avoiding unfaithful toxic therapies. Citation Format: Francesca Citron, Gian Luca Rampioni Vinciguerra, Giuseppe Fanetti, Ilenia Segatto, Barbara Belletti, Andrea Vecchione, Giovanni Franchin, Gustavo Baldassarre. miR-9 expression regulates and predicts the response to EGFR inhibitors in head &amp; neck squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3128.

Requirements for Documenting Electrical Cell Stimulation Experiments for Replicability and Numerical Modeling∗.

Thorough documentation of biological experiments is necessary for their replicability. This becomes even more evident when individual steps of in vitro wet-lab experiments are to be incorporated into computer simulation models. In the highly interdisciplinary field of electrical stimulation of biological cells, not only biological but also physical aspects play a crucial role. Simulations may help to identify parameters that influence cells and thereby reveal new insights into mechanisms of the cell biological system. However, missing or misleading documentation of the electrical stimulation step within wet-lab experiments may lead to discrepancies between reported and simulated electrical quantities. In addition, this threatens the replicability of electrical stimulation experiments. Thus, we argue that a minimal set of information is needed to enable a translation of electrical stimulation experiments of biological cells into computer simulation experiments and to support replicability. This set includes detailed information about the electronic devices and components, their set-up as well as the applied stimulus and shall be integrated into an existing guideline for cell biological experiments. Ideally, the documentation should also contain measured properties of the cellular and experimental environment. Furthermore, a realization of our proposed documentation requirements within electronic lab notebooks may provide a crucial step toward a more seamless integration of wet-lab data into simulations. Based on two exemplary studies, we demonstrate the relevance of our claim.

Ficus extract-A promising agent for antimammary tumorigenesis: A review on current status and future possibilities.

Pharmacological studies have shown that various species of Ficus have antiviral, antidiarrheal, antipyretic, hypolipidemic, antidiabetic, antioxidant, anticancer, antiparasitic, antiangiogenic, anti-inflammatory, antibacterial, antiplatelet, reproductive, dermatological, immunological, endocrine, and hepato and nephron protective effects. But there is no sufficient research on biomolecules present in the leaf extract of Ficus religiosa and its mechanism of action. We have previously reported that bioavailable constituents of F.religiosa leaf extract exert photosensitizing and apoptosis-inducing capability through the generation of intracellular reactive oxygen species on breast cancer cells. In this review, we have evaluated the expression of checkpoint proteins of G1/S and sub G0 phase with wet lab data and also have done a data mining of other research for other potential mechanistic action of the F.religiosa leaf extract.

PAP genes are tissue- and cell-specific markers of chloroplast development

Expression of PAP genes is strongly coordinated and represents a highly selective cell-specific marker associated with the development of chloroplasts in photosynthetically active organs of Arabidopsis seedlings and adult plants. Transcription in plastids of plants depends on the activity of phage-type single-subunit nuclear-encoded RNA polymerases (NEP) and a prokaryotic multi-subunit plastid-encoded RNA polymerase (PEP). PEP is comprised of the core subunits α, β, β' and β″ encoded by rpoA, rpoB/C1/C2 genes located on the plastome. This core enzyme needs to interact with nuclear-encoded sigma factors for proper promoter recognition. In chloroplasts, the core enzyme is surrounded by additional 12 nuclear-encoded subunits, all of eukaryotic origin. These PEP-associated proteins (PAPs) were found to be essential for chloroplast biogenesis as Arabidopsis inactivation mutants for each of them revealed albino or pale-green phenotypes. In silico analysis of transcriptomic data suggests that PAP genes represent a tightly controlled regulon, whereas wetlab data are sparse and correspond to the expression of individual genes mostly studied at the seedling stage. Using RT-PCR, transient, and stable expression assays of PAP promoter-GUS-constructs, we do provide, in this study, a comprehensive expression catalogue for PAP genes throughout the life cycle of Arabidopsis. We demonstrate a selective impact of light on PAP gene expression and uncover a high tissue specificity that is coupled to developmental progression especially during the transition from skotomorphogenesis to photomorphogenesis. Our data imply that PAP gene expression precedes the formation of chloroplasts rendering PAP genes a tissue- and cell-specific marker of chloroplast biogenesis.

Codon usage and amino acid usage influence genes expression level.

Highly expressed genes in any species differ in the usage frequency of synonymous codons. The relative recurrence of an event of the favored codon pair (amino acid pairs) varies between gene and genomes due to varying gene expression and different base composition. Here we propose a new measure for predicting the gene expression level, i.e., codon plus amino bias index (CABI). Our approach is based on the relative bias of the favored codon pair inclination among the genes, illustrated by analyzing the CABI score of the Medicago truncatula genes. CABI showed strong correlation with all other widely used measures (CAI, RCBS, SCUO) for gene expression analysis. Surprisingly, CABI outperforms all other measures by showing better correlation with the wet-lab data. This emphasizes the importance of the neighboring codons of the favored codon in a synonymous group while estimating the expression level of a gene.

Reproducibility and Comparability of Computational Models for Astrocyte Calcium Excitability.

The scientific community across all disciplines faces the same challenges of ensuring accessibility, reproducibility, and efficient comparability of scientific results. Computational neuroscience is a rapidly developing field, where reproducibility and comparability of research results have gained increasing interest over the past years. As the number of computational models of brain functions is increasing, we chose to address reproducibility using four previously published computational models of astrocyte excitability as an example. Although not conventionally taken into account when modeling neuronal systems, astrocytes have been shown to take part in a variety of in vitro and in vivo phenomena including synaptic transmission. Two of the selected astrocyte models describe spontaneous calcium excitability, and the other two neurotransmitter-evoked calcium excitability. We specifically addressed how well the original simulation results can be reproduced with a reimplementation of the models. Additionally, we studied how well the selected models can be reused and whether they are comparable in other stimulation conditions and research settings. Unexpectedly, we found out that three of the model publications did not give all the necessary information required to reimplement the models. In addition, we were able to reproduce the original results of only one of the models completely based on the information given in the original publications and in the errata. We actually found errors in the equations provided by two of the model publications; after modifying the equations accordingly, the original results were reproduced more accurately. Even though the selected models were developed to describe the same biological event, namely astrocyte calcium excitability, the models behaved quite differently compared to one another. Our findings on a specific set of published astrocyte models stress the importance of proper validation of the models against experimental wet-lab data from astrocytes as well as the careful review process of models. A variety of aspects of model development could be improved, including the presentation of models in publications and databases. Specifically, all necessary mathematical equations, as well as parameter values, initial values of variables, and stimuli used should be given precisely for successful reproduction of scientific results.