Alexander Aliper

Genetics and epigenetics of aging and longevity

Evolutionary theories of aging predict the existence of certain genes that provide selective advantage early in life with adverse effect on lifespan later in life (antagonistic pleiotropy theory) or longevity insurance genes (disposable soma theory). Indeed, the study of human and animal genetics is gradually identifying new genes that increase lifespan when overexpressed or mutated: gerontogenes. Furthermore, genetic and epigenetic mechanisms are being identified that have a positive effect on longevity. The gerontogenes are classified as lifespan regulators, mediators, effectors, housekeeping genes, genes involved in mitochondrial function, and genes regulating cellular senescence and apoptosis. In this review we demonstrate that the majority of the genes as well as genetic and epigenetic mechanisms that are involved in regulation of longevity are highly interconnected and related to stress response.

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics, and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from the LINCS Project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled data set of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both pathway and gene level classification, DNN achieved high classification accuracy and convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem, however, models based on pathway level data performed significantly better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.

Deep biomarkers of human aging: Application of deep neural networks to biomarker development

One of the major impediments in human aging research is the absence of a comprehensive and actionable set of biomarkers that may be targeted and measured to track the effectiveness of therapeutic interventions. In this study, we designed a modular ensemble of 21 deep neural networks (DNNs) of varying depth, structure and optimization to predict human chronological age using a basic blood test. To train the DNNs, we used over 60,000 samples from common blood biochemistry and cell count tests from routine health exams performed by a single laboratory and linked to chronological age and sex. The best performing DNN in the ensemble demonstrated 81.5 % epsilon-accuracy r = 0.90 with R2 = 0.80 and MAE = 6.07 years in predicting chronological age within a 10 year frame, while the entire ensemble achieved 83.5% epsilon-accuracy r = 0.91 with R2 = 0.82 and MAE = 5.55 years. The ensemble also identified the 5 most important markers for predicting human chronological age: albumin, glucose, alkaline phosphatase, urea and erythrocytes. To allow for public testing and evaluate real-life performance of the predictor, we developed an online system available at http://www.aging.ai. The ensemble approach may facilitate integration of multi-modal data linked to chronological age and sex that may lead to simple, minimally invasive, and affordable methods of tracking integrated biomarkers of aging in humans and performing cross-species feature importance analysis.

Molecular aspects of development and regulation of endometriosis.

Endometriosis is a common and painful condition affecting women of reproductive age. While the underlying pathophysiology is still largely unknown, much advancement has been made in understanding the progression of the disease. In recent years, a great deal of research has focused on non-invasive diagnostic tools, such as biomarkers, as well as identification of potential therapeutic targets. In this article, we will review the etiology and cellular mechanisms associated with endometriosis as well as the current diagnostic tools and therapies. We will then discuss the more recent genomic and proteomic studies and how these data may guide development of novel diagnostics and therapeutics. The current diagnostic tools are invasive and current therapies primarily treat the symptoms of endometriosis. Optimally, the advancement of “-omic” data will facilitate the development of non-invasive diagnostic biomarkers as well as therapeutics that target the pathophysiology of the disease and halt, or even reverse, progression. However, the amount of data generated by these types of studies is vast and bioinformatics analysis, such as we present here, will be critical to identification of appropriate targets for further study.

Human-specific endogenous retroviral insert serves as an enhancer for the schizophrenia-linked gene PRODH

Significance We identified a human-specific endogenous retroviral insert (hsERV) that acts as an enhancer for human PRODH, hsERV_PRODH. PRODH encodes proline dehydrogenase, which is involved in neuromediator synthesis in the CNS. We show that the hsERV_PRODH enhancer acts synergistically with the CpG island of PRODH and is regulated by methylation. We detected high PRODH expression in the hippocampus, which was correlated with the undermethylated state of this enhancer. PRODH regulatory elements provide neuron-specific transcription in hippocampal cells, and the mechanism of hsERV_PRODH enhancer activity involves the binding of transcriptional factor SOX2. Because PRODH is associated with several neurological disorders, we hypothesize that the human-specific regulation of PRODH by hsERV_PRODH may have played a role in human evolution by upregulating the expression of this important CNS-specific gene. Using a systematic, whole-genome analysis of enhancer activity of human-specific endogenous retroviral inserts (hsERVs), we identified an element, hsERVPRODH, that acts as a tissue-specific enhancer for the PRODH gene, which is required for proper CNS functioning. PRODH is one of the candidate genes for susceptibility to schizophrenia and other neurological disorders. It codes for a proline dehydrogenase enzyme, which catalyses the first step of proline catabolism and most likely is involved in neuromediator synthesis in the CNS. We investigated the mechanisms that regulate hsERVPRODH enhancer activity. We showed that the hsERVPRODH enhancer and the internal CpG island of PRODH synergistically activate its promoter. The enhancer activity of hsERVPRODH is regulated by methylation, and in an undermethylated state it can up-regulate PRODH expression in the hippocampus. The mechanism of hsERVPRODH enhancer activity involves the binding of the transcription factor SOX2, whch is preferentially expressed in hippocampus. We propose that the interaction of hsERVPRODH and PRODH may have contributed to human CNS evolution.

The OncoFinder algorithm for minimizing the errors introduced by the high-throughput methods of transcriptome analysis.

The diversity of the installed sequencing and microarray equipment make it increasingly difficult to compare and analyze the gene expression datasets obtained using the different methods. Many applications requiring high-quality and low error rates cannot make use of available data using traditional analytical approaches. Recently, we proposed a new concept of signalome-wide analysis of functional changes in the intracellular pathways termed OncoFinder, a bioinformatic tool for quantitative estimation of the signaling pathway activation (SPA). We also developed methods to compare the gene expression data obtained using multiple platforms and minimizing the error rates by mapping the gene expression data onto the known and custom signaling pathways. This technique for the first time makes it possible to analyze the functional features of intracellular regulation on a mathematical basis. In this study we show that the OncoFinder method significantly reduces the errors introduced by transcriptome-wide experimental techniques. We compared the gene expression data for the same biological samples obtained by both the next generation sequencing (NGS) and microarray methods. For these different techniques we demonstrate that there is virtually no correlation between the gene expression values for all datasets analyzed (R2 < 0.1). In contrast, when the OncoFinder algorithm is applied to the data we observed clear-cut correlations between the NGS and microarray gene expression datasets. The SPA profiles obtained using NGS and microarray techniques were almost identical for the same biological samples allowing for the platform-agnostic analytical applications. We conclude that this feature of the OncoFinder enables to characterize the functional states of the transcriptomes and interactomes more accurately as before, which makes OncoFinder a method of choice for many applications including genetics, physiology, biomedicine, and molecular diagnostics.

Silencing AML1-ETO gene expression leads to simultaneous activation of both pro-apoptotic and proliferation signaling

The t(8;21)(q22;q22) rearrangement represents the most common chromosomal translocation in acute myeloid leukemia (AML). It results in a transcript encoding for the fusion protein AML1-ETO (AE) with transcription factor activity. AE is considered to be an attractive target for treating t(8;21) leukemia. However, AE expression alone is insufficient to cause transformation, and thus the potential of such therapy remains unclear. Several genes are deregulated in AML cells, including KIT that encodes a tyrosine kinase receptor. Here, we show that AML cells transduced with short hairpin RNA vector targeting AE mRNAs have a dramatic decrease in growth rate that is caused by induction of apoptosis and deregulation of the cell cycle. A reduction in KIT mRNA levels was also observed in AE-silenced cells, but silencing KIT expression reduced cell growth but did not induce apoptosis. Transcription profiling of cells that escape cell death revealed activation of a number of signaling pathways involved in cell survival and proliferation. In particular, we find that the extracellular signal-regulated kinase 2 (ERK2; also known as mitogen-activated protein kinase 1 (MAPK1)) protein could mediate activation of 23 out of 29 (79%) of these upregulated pathways and thus may be regarded as the key player in establishing the t(8;21)-positive leukemic cells resistant to AE suppression.

A role for G‐CSF and GM‐CSF in nonmyeloid cancers

Granulocyte colony‐stimulating factor (G‐CSF) and granulocyte‐macrophage colony‐stimulating factor (GM‐CSF) modulate progression of certain solid tumors. The G‐CSF‐ or GM‐CSF‐secreting cancers, albeit not very common are, however, among the most rapidly advancing ones due to a cytokine‐mediated immune suppression and angiogenesis. Similarly, de novo angiogenesis and vasculogenesis may complicate adjuvant use of recombinant G‐CSF or GM‐CSF thus possibly contributing to a cancer relapse. Rapid diagnostic tools to differentiate G‐CSF‐ or GM‐CSF‐secreting cancers are not well developed therefore hindering efforts to individualize treatments for these patients. Given an increasing utilization of adjuvant G‐/GM‐CSF in cancer therapy, we aimed to summarize recent studies exploring their roles in pathophysiology of solid tumors and to provide insights into some complexities of their therapeutic applications.

Pathway activation strength is a novel independent prognostic biomarker for cetuximab sensitivity in colorectal cancer patients

Cetuximab, a monoclonal antibody against epidermal growth factor receptor (EGFR), was shown to be active in colorectal cancer. Although some patients who harbor K-ras wild-type tumors benefit from cetuximab treatment, 40 to 60% of patients with wild-type K-ras tumors do not respond to cetuximab. Currently, there is no universal marker or method of clinical utility that could guide the treatment of cetuximab in colorectal cancer. Here, we demonstrate a method to predict response to cetuximab in patients with colorectal cancer using OncoFinder pathway activation strength (PAS), based on the transcriptomic data of the tumors. We first evaluated our OncoFinder pathway activation strength model in a set of transcriptomic data obtained from patient-derived xenograft (PDx) models established from colorectal cancer biopsies. Then, the approach and models were validated using a clinical trial data set. PAS could efficiently predict patients’ response to cetuximab, and thus holds promise as a selection criterion for cetuximab treatment in metastatic colorectal cancer.

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input. In this paper we present the first application of generative adversarial autoencoders (AAE) for generating novel molecular fingerprints with a defined set of parameters. We developed a 7-layer AAE architecture with the latent middle layer serving as a discriminator. As an input and output the AAE uses a vector of binary fingerprints and concentration of the molecule. In the latent layer we also introduced a neuron responsible for growth inhibition percentage, which when negative indicates the reduction in the number of tumor cells after the treatment. To train the AAE we used the NCI-60 cell line assay data for 6252 compounds profiled on MCF-7 cell line. The output of the AAE was used to screen 72 million compounds in PubChem and select candidate molecules with potential anti-cancer properties. This approach is a proof of concept of an artificially-intelligent drug discovery engine, where AAEs are used to generate new molecular fingerprints with the desired molecular properties.