Analysis of transcriptomic responses to SARS-CoV-2 reveals plausible defective pathways responsible for increased susceptibility to infection and complications and helps to develop fast-track repositioning of drugs against COVID-19.

BACKGROUND: To understand the transcriptomic response to SARS-CoV-2 infection, is of the utmost importance to design diagnostic tools predicting the severity of the infection. METHODS: We have performed a deep sampling analysis of the viral transcriptomic data oriented towards drug repositioning. Using different samplers, the basic principle of this methodology the biological invariance, which means that the pathways altered by the disease, should be independent on the algorithm used to unravel them. RESULTS: The transcriptomic analysis of the altered pathways, reveals a distinctive inflammatory response and potential side effects of infection. The virus replication causes, in some cases, acute respiratory distress syndrome in the lungs, and affects other organs such as heart, brain, and kidneys. Therefore, the repositioned drugs to fight COVID-19 should, not only target the interferon signalling pathway and the control of the inflammation, but also the altered genetic pathways related to the side effects of infection. We also show via Principal Component Analysis that the transcriptome signatures are different from influenza and RSV. The gene COL1A1, which controls collagen production, seems to play a key/vital role in the regulation of the immune system. Additionally, other small-scale signature genes appear to be involved in the development of other COVID-19 comorbidities. CONCLUSIONS: Transcriptome-based drug repositioning offers possible fast-track antiviral therapy for COVID-19 patients. It calls for additional clinical studies using FDA approved drugs for patients with increased susceptibility to infection and with serious medical complications.

Robust Sampling of Defective Pathways in Alzheimer's Disease. Implications in Drug Repositioning.

We present the analysis of the defective genetic pathways of the Late-Onset Alzheimer's Disease (LOAD) compared to the Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using different sampling methodologies. These algorithms sample the uncertainty space that is intrinsic to any kind of highly underdetermined phenotype prediction problem, by looking for the minimum-scale signatures (header genes) corresponding to different random holdouts. The biological pathways can be identified performing posterior analysis of these signatures established via cross-validation holdouts and plugging the set of most frequently sampled genes into different ontological platforms. That way, the effect of helper genes, whose presence might be due to the high degree of under determinacy of these experiments and data noise, is reduced. Our results suggest that common pathways for Alzheimer's disease and MCI are mainly related to viral mRNA translation, influenza viral RNA transcription and replication, gene expression, mitochondrial translation, and metabolism, with these results being highly consistent regardless of the comparative methods. The cross-validated predictive accuracies achieved for the LOAD and MCI discriminations were 84% and 81.5%, respectively. The difference between LOAD and MCI could not be clearly established (74% accuracy). The most discriminatory genes of the LOAD-MCI discrimination are associated with proteasome mediated degradation and G-protein signaling. Based on these findings we have also performed drug repositioning using Dr. Insight package, proposing the following different typologies of drugs: isoquinoline alkaloids, antitumor antibiotics, phosphoinositide 3-kinase PI3K, autophagy inhibitors, antagonists of the muscarinic acetylcholine receptor and histone deacetylase inhibitors. We believe that the potential clinical relevance of these findings should be further investigated and confirmed with other independent studies.

Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques.

We discuss the use of the regularized linear discriminant analysis (LDA) as a model reduction technique combined with particle swarm optimization (PSO) in protein tertiary structure prediction, followed by structure refinement based on singular value decomposition (SVD) and PSO. The algorithm presented in this paper corresponds to the category of template-based modeling. The algorithm performs a preselection of protein templates before constructing a lower dimensional subspace via a regularized LDA. The protein coordinates in the reduced spaced are sampled using a highly explorative optimization algorithm, regressive-regressive PSO (RR-PSO). The obtained structure is then projected onto a reduced space via singular value decomposition and further optimized via RR-PSO to carry out a structure refinement. The final structures are similar to those predicted by best structure prediction tools, such as Rossetta and Zhang servers. The main advantage of our methodology is that alleviates the ill-posed character of protein structure prediction problems related to high dimensional optimization. It is also capable of sampling a wide range of conformational space due to the application of a regularized linear discriminant analysis, which allows us to expand the differences over a reduced basis set.

On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine.

The complexity of orphan diseases, which are those that do not have an effective treatment, together with the high dimensionality of the genetic data used for their analysis and the high degree of uncertainty in the understanding of the mechanisms and genetic pathways which are involved in their development, motivate the use of advanced techniques of artificial intelligence and in-depth knowledge of molecular biology, which is crucial in order to find plausible solutions in drug design, including drug repositioning. Particularly, we show that the use of robust deep sampling methodologies of the altered genetics serves to obtain meaningful results and dramatically decreases the cost of research and development in drug design, influencing very positively the use of precision medicine and the outcomes in patients. The target-centric approach and the use of strong prior hypotheses that are not matched against reality (disease genetic data) are undoubtedly the cause of the high number of drug design failures and attrition rates. Sampling and prediction under uncertain conditions cannot be avoided in the development of precision medicine.

Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer.

BACKGROUND: Phenotype prediction problems are usually considered ill-posed, as the amount of samples is very limited with respect to the scrutinized genetic probes. This fact complicates the sampling of the defective genetic pathways due to the high number of possible discriminatory genetic networks involved. In this research, we outline three novel sampling algorithms utilized to identify, classify and characterize the defective pathways in phenotype prediction problems, such as the Fisher's ratio sampler, the Holdout sampler and the Random sampler, and apply each one to the analysis of genetic pathways involved in tumor behavior and outcomes of triple negative breast cancers (TNBC). Altered biological pathways are identified using the most frequently sampled genes and are compared to those obtained via Bayesian Networks (BNs). RESULTS: Random, Fisher's ratio and Holdout samplers were more accurate and robust than BNs, while providing comparable insights about disease genomics. CONCLUSIONS: The three samplers tested are good alternatives to Bayesian Networks since they are less computationally demanding algorithms. Importantly, this analysis confirms the concept of "biological invariance" since the altered pathways should be independent of the sampling methodology and the classifier used for their inference. Nevertheless, still some modifications are needed in the Bayesian networks to be able to sample correctly the uncertainty space in phenotype prediction problems, since the probabilistic parameterization of the uncertainty space is not unique and the use of the optimum network might falsify the pathways analysis.

Using artificial intelligence methods to speed up drug discovery.

Introduction: Drug discovery is the process through which potential new compounds are identified by means of biology, chemistry, and pharmacology. Due to the high complexity of genomic data, AI techniques are increasingly needed to help reduce this and aid the adoption of optimal decisions. Phenotypic prediction is of particular use to drug discovery and precision medicine where sets of genes that predict a given phenotype are determined. Phenotypic prediction is an undetermined problem given that the number of monitored genetic probes markedly exceeds the number of collected samples (from patients). This imbalance creates ambiguity in the characterization of the biological pathways that are responsible for disease development. Areas covered: In this paper, the authors present AI methodologies that perform a robust deep sampling of altered genetic pathways to locate new therapeutic targets, assist in drug repurposing and speed up and optimize the drug selection process. Expert opinion: AI is a potential solution to a number of drug discovery problems, though one should, bear in mind that the quality of data predicts the overall quality of the prediction, as in any modeling task in data science. The use of transparent methodologies is crucial, particularly in drug repositioning/repurposing in rare diseases.

Robust Prediction of Single and Multiple Point Protein Mutations Stability Changes.

Accurate prediction of protein stability changes resulting from amino acid substitutions is of utmost importance in medicine to better understand which mutations are deleterious, leading to diseases, and which are neutral. Since conducting wet lab experiments to get a better understanding of protein mutations is costly and time consuming, and because of huge number of possible mutations the need of computational methods that could accurately predict effects of amino acid mutations is of greatest importance. In this research, we present a robust methodology to predict the energy changes of a proteins upon mutations. The proposed prediction scheme is based on two step algorithm that is a Holdout Random Sampler followed by a neural network model for regression. The Holdout Random Sampler is utilized to analysis the energy change, the corresponding uncertainty, and to obtain a set of admissible energy changes, expressed as a cumulative distribution function. These values are further utilized to train a simple neural network model that can predict the energy changes. Results were blindly tested (validated) against experimental energy changes, giving Pearson correlation coefficients of 0.66 for Single Point Mutations and 0.77 for Multiple Point Mutations. These results confirm the successfulness of our method, since it outperforms majority of previous studies in this field.

Particle Swarm Optimization and Uncertainty Assessment in Inverse Problems.

Most inverse problems in the industry (and particularly in geophysical exploration) are highly underdetermined because the number of model parameters too high to achieve accurate data predictions and because the sampling of the data space is scarce and incomplete; it is always affected by different kinds of noise. Additionally, the physics of the forward problem is a simplification of the reality. All these facts result in that the inverse problem solution is not unique; that is, there are different inverse solutions (called equivalent), compatible with the prior information that fits the observed data within similar error bounds. In the case of nonlinear inverse problems, these equivalent models are located in disconnected flat curvilinear valleys of the cost-function topography. The uncertainty analysis consists of obtaining a representation of this complex topography via different sampling methodologies. In this paper, we focus on the use of a particle swarm optimization (PSO) algorithm to sample the region of equivalence in nonlinear inverse problems. Although this methodology has a general purpose, we show its application for the uncertainty assessment of the solution of a geophysical problem concerning gravity inversion in sedimentary basins, showing that it is possible to efficiently perform this task in a sampling-while-optimizing mode. Particularly, we explain how to use and analyze the geophysical models sampled by exploratory PSO family members to infer different descriptors of nonlinear uncertainty.

Modeling the Effect of Polymer Chain Stiffness on the Behavior of Polymer Nanocomposites.

Due to their central role in industrial formulations spanning from food packaging to smart coatings, polymer nanocomposites have been the object of remarkable attention over the last two decades. Incorporating nanoparticles (NPs) into a polymer matrix modifies the conformation and mobility of the polymer chains at the NP-polymer interface and can potentially provide materials with enhanced properties as compared to pristine polymers. To this end, it is crucial to predict and control the ability of NPs to diffuse and achieve a good dispersion in the polymer matrix. Understanding how to control the NPs' dispersion is a challenging task controlled by the delicate balance between enthalpic and entropic contributions, such as NP-polymer interaction, NP size and shape, and polymer chain conformation. By performing molecular dynamics (MD) simulations, we investigate the effect of polymer chains' stiffness on the mobility of spherical NPs that establish weak or strong interactions with the polymer. Our results show a sound dependence of the NPs' diffusivity on the long-range order of the polymer melt, which undergoes an isotropic-to-nematic phase transition upon increasing chain stiffness. This phase transition induces a dynamical anisotropy in the nematic phase, with the NPs preferentially diffusing along the nematic director rather than in the directions perpendicular to it. Not only does this tendency determine the NPs' mobility and degree of dispersion in the polymer matrix, but it also influences the resistance to flow of the polymer nanocomposite when a shear is applied. In particular, to assess the role of the chains' conformation on the macroscopic response of our model PNC, we employ reverse nonequilibrium MD to calculate the zero-shear viscosity in both the isotropic and nematic phases, and unveil a plasticizing effect at increasing chain stiffness when the shear is applied along the nematic axis.