Deep learning (DL) has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and biomedical data. A number of comprehensive reviews have been published on such applications, ranging from high-level reviews with future perspectives to those mainly serving as tutorials. These reviews have provided an excellent introduction to and guideline for applications of DL in bioinformatics, covering multiple types of machine learning (ML) problems, different DL architectures, and ranges of biological/biomedical problems. However, most of these reviews have focused on previous research, whereas current trends in the principled DL field and perspectives on their future developments and potential new applications to biology and biomedicine are still scarce. We will focus on modern DL, the ongoing trends and future directions of the principled DL field, and postulate new and major applications in bioinformatics.

Understanding ethanol-induced stresses and responses in biofuel-producing bacteria at systems level has significant implications in engineering more efficient biofuel producers. We present a computational study of transcriptomic and genomic data of both ethanol-stressed and ethanol-adapted E. coli cells with computationally predicated ethanol-binding proteins and experimentally identified ethanol tolerance genes. Our analysis suggests: (1) ethanol damages cell wall and membrane integrity, causing increased stresses, particularly reactive oxygen species, which damages DNA and reduces the O₂ level; (2) decreased cross-membrane proton gradient from membrane damage, coupled with hypoxia, leads to reduced ATP production by aerobic respiration, driving cells to rely more on fatty acid oxidation, anaerobic respiration and fermentation for ATP production; (3) the reduced ATP generation results in substantially decreased synthesis of macromolecules; (4) ethanol can directly bind 213 proteins including transcription factors, altering their functions; (5) all these changes together induce multiple stress responses, reduced biosynthesis, cell viability and growth; and (6) ethanol-adapted E. coli cells restore the majority of these reduced activities through selection of specific genomic mutations and alteration of stress responses, ultimately restoring normal ATP production, macromolecule biosynthesis, and growth. These new insights into the energy and mass balance will inform design of more ethanol-tolerant strains.

It has been observed that both cancer tissue cells and normal proliferating cells (NPCs) have the Warburg effect. Our goal here is to demonstrate that they do this for different reasons. To accomplish this, we have analyzed the transcriptomic data of over 7000 cancer and control tissues of 14 cancer types in TCGA and data of five NPC types in GEO. Our analyses reveal that NPCs accumulate large quantities of ATPs produced by the respiration process before starting the Warburg effect, to raise the intracellular pH from ∼6.8 to ∼7.2 and to prepare for cell division energetically. Once cell cycle starts, the cells start to rely on glycolysis for ATP generation followed by ATP hydrolysis and lactic acid release, to maintain the elevated intracellular pH as needed by cell division since together the three processes are pH neutral. The cells go back to the normal respiration-based ATP production once the cell division phase ends. In comparison, cancer cells have reached their intracellular pH at ∼7.4 from top down as multiple acid-loading transporters are up-regulated and most acid-extruding ones except for lactic acid exporters are repressed. Cancer cells use continuous glycolysis for ATP production as way to acidify the intracellular space since the lactic acid secretion is decoupled from glycolysis-based ATP generation and is pH balanced by increased expressions of acid-loading transporters. Co-expression analyses suggest that lactic acid secretion is regulated by external, non-pH related signals. Overall, our data strongly suggest that the two cell types have the Warburg effect for very different reasons.

Considerable metabolic reprogramming has been observed in a conserved manner across multiple cancer types, but their true causes remain elusive. We present an analysis of around 50 such reprogrammed metabolisms (RM) including the Warburg effect, nucleotide de novo synthesis, and sialic acid biosynthesis in cancer. Analyses of the biochemical reactions conducted by these RMs, coupled with gene expression data of their catalyzing enzymes, in 7,011 tissues of 14 cancer types, revealed that all RMs produce more H+ than their original metabolisms. These data strongly support a model that these RMs are induced or selected to neutralize a persistent intracellular alkaline stress due to chronic inflammation and local iron overload. To sustain these RMs for survival, cells must find metabolic exits for the nonproton products of these RMs in a continuous manner, some of which pose major challenges, such as nucleotides and sialic acids, because they are electrically charged. This analysis strongly suggests that continuous cell division and other cancerous behaviors are ways for the affected cells to remove such products in a timely and sustained manner. As supporting evidence, this model can offer simple and natural explanations to a range of long-standing open questions in cancer research including the cause of the Warburg effect. Significance: Inhibiting acidifying metabolic reprogramming could be a novel strategy for treating cancer.

We present a computational study of tissue transcriptomic data of 14 cancer types to address: what may drive cancer cell division? Our analyses point to that persistent disruption of the intracellular pH by Fenton reactions may be at the root of cancer development. Specifically, we have statistically demonstrated that Fenton reactions take place in cancer cytosol and mitochondria across all the 14 cancer types, based on cancer tissue gene-expression data integrated via the Michaelis–Menten equation. In addition, we have shown that (i) Fenton reactions in cytosol of the disease cells will continuously increase their pH, to which the cells respond by generating net protons to keep the pH stable through a combination of synthesizing glycolytic ATPs and consuming them by nucleotide syntheses, which may drive cell division to rid of the continuously synthesized nucleotides; and (ii) Fenton reactions in mitochondria give rise to novel ways for ATP synthesis with electrons ultimately coming from H₂O₂, largely originated from immune cells. A model is developed to link these to cancer development, where some mutations may be selected to facilitate cell division at rates dictated by Fenton reactions.

Lymph node metastasis of lung cancer is a serious problem. Therefore, there is a need for a detailed transcriptome study of metastatic lung adenocarcinoma. The lung adenocarcinoma RNA-seq data and the corresponding clinical information available from TCGA were analyzed. Differential expression, gradient changes, and biological pathways were carried out. Potential gene(s) associated with tumor metastasis and survival were validated by Cox regression. A total of 406 and 439 differentially expressed genes were identified for lymph node metastasis and TNM stages, respectively. Of the 296 intersection genes, 112 were associated with nodal metastasis and/or staging. Only 25 of these 112 genes with gradient changes were involved in nodal metastasis, and 13 were involved in staging. Only one gene, RN7SL494P, might be involved in lung adenocarcinoma development and poor outcome. Finally, Cox regression results verified that age, pathology classification, radiotherapy and chemotherapy are all the independent prognostic factors. In particular, RN7SL494P was further verified to be an independent factor affecting lymph node metastasis and patient survival. Furthermore, we verified the RN7SL494P function using simulation data generated by mixing cell lines of the Cancer Cell Line Encyclopedia (CCLE) and obtained consistent results. Our findings suggest a potential clinical application of the RN7SL494P as a promising marker in the evaluation of patients with primary lung adenocarcinoma, not only for predicting nodal metastasis, but also for the prognosis of the outcome.