High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases.

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.

The Relaxin-3 Receptor, RXFP3, Is a Modulator of Aging-Related Disease.

During the aging process our body becomes less well equipped to deal with cellular stress, resulting in an increase in unrepaired damage. This causes varying degrees of impaired functionality and an increased risk of mortality. One of the most effective anti-aging strategies involves interventions that combine simultaneous glucometabolic support with augmented DNA damage protection/repair. Thus, it seems prudent to develop therapeutic strategies that target this combinatorial approach. Studies have shown that the ADP-ribosylation factor (ARF) GTPase activating protein GIT2 (GIT2) acts as a keystone protein in the aging process. GIT2 can control both DNA repair and glucose metabolism. Through in vivo co-regulation analyses it was found that GIT2 forms a close coexpression-based relationship with the relaxin-3 receptor (RXFP3). Cellular RXFP3 expression is directly affected by DNA damage and oxidative stress. Overexpression or stimulation of this receptor, by its endogenous ligand relaxin 3 (RLN3), can regulate the DNA damage response and repair processes. Interestingly, RLN3 is an insulin-like peptide and has been shown to control multiple disease processes linked to aging mechanisms, e.g., anxiety, depression, memory dysfunction, appetite, and anti-apoptotic mechanisms. Here we discuss the molecular mechanisms underlying the various roles of RXFP3/RLN3 signaling in aging and age-related disorders.

Making Biomedical Sciences publications more accessible for machines.

With the rapidly expanding catalogue of scientific publications, especially within the Biomedical Sciences field, it is becoming increasingly difficult for researchers to search for, read or even interpret emerging scientific findings. PubMed, just one of the current biomedical data repositories, comprises over 33 million citations for biomedical research, and over 2500 publications are added each day. To further strengthen the impact biomedical research, we suggest that there should be more synergy between publications and machines. By bringing machines into the realm of research and publication, we can greatly augment the assessment, investigation and cataloging of the biomedical literary corpus. The effective application of machine-based manuscript assessment and interpretation is now crucial, and potentially stands as the most effective way for researchers to comprehend and process the tsunami of biomedical data and literature. Many biomedical manuscripts are currently published online in poorly searchable document types, with figures and data presented in formats that are partially inaccessible to machine-based approaches. The structure and format of biomedical manuscripts should be adapted to facilitate machine-assisted interrogation of this important literary corpus. In this context, it is important to embrace the concept that biomedical scientists should also write manuscripts that can be read by machines. It is likely that an enhanced human-machine synergy in reading biomedical publications will greatly enhance biomedical data retrieval and reveal novel insights into complex datasets.

Altered learning, memory, and social behavior in type 1 taste receptor subunit 3 knock-out mice are associated with neuronal dysfunction.

The type 1 taste receptor member 3 (T1R3) is a G protein-coupled receptor involved in sweet-taste perception. Besides the tongue, the T1R3 receptor is highly expressed in brain areas implicated in cognition, including the hippocampus and cortex. As cognitive decline is often preceded by significant metabolic or endocrinological dysfunctions regulated by the sweet-taste perception system, we hypothesized that a disruption of the sweet-taste perception in the brain could have a key role in the development of cognitive dysfunction. To assess the importance of the sweet-taste receptors in the brain, we conducted transcriptomic and proteomic analyses of cortical and hippocampal tissues isolated from T1R3 knock-out (T1R3KO) mice. The effect of an impaired sweet-taste perception system on cognition functions were examined by analyzing synaptic integrity and performing animal behavior on T1R3KO mice. Although T1R3KO mice did not present a metabolically disrupted phenotype, bioinformatic interpretation of the high-dimensionality data indicated a strong neurodegenerative signature associated with significant alterations in pathways involved in neuritogenesis, dendritic growth, and synaptogenesis. Furthermore, a significantly reduced dendritic spine density was observed in T1R3KO mice together with alterations in learning and memory functions as well as sociability deficits. Taken together our data suggest that the sweet-taste receptor system plays an important neurotrophic role in the extralingual central nervous tissue that underpins synaptic function, memory acquisition, and social behavior.

Intelligent and effective informatic deconvolution of "Big Data" and its future impact on the quantitative nature of neurodegenerative disease therapy.

Biomedical data sets are becoming increasingly larger and a plethora of high-dimensionality data sets ("Big Data") are now freely accessible for neurodegenerative diseases, such as Alzheimer's disease. It is thus important that new informatic analysis platforms are developed that allow the organization and interrogation of Big Data resources into a rational and actionable mechanism for advanced therapeutic development. This will entail the generation of systems and tools that allow the cross-platform correlation between data sets of distinct types, for example, transcriptomic, proteomic, and metabolomic. Here, we provide a comprehensive overview of the latest strategies, including latent semantic analytics, topological data investigation, and deep learning techniques that will drive the future development of diagnostic and therapeutic applications for Alzheimer's disease. We contend that diverse informatic "Big Data" platforms should be synergistically designed with more advanced chemical/drug and cellular/tissue-based phenotypic analytical predictive models to assist in either de novo drug design or effective drug repurposing.

G Protein-Coupled Receptor Systems as Crucial Regulators of DNA Damage Response Processes.

G protein-coupled receptors (GPCRs) and their associated proteins represent one of the most diverse cellular signaling systems involved in both physiological and pathophysiological processes. Aging represents perhaps the most complex biological process in humans and involves a progressive degradation of systemic integrity and physiological resilience. This is in part mediated by age-related aberrations in energy metabolism, mitochondrial function, protein folding and sorting, inflammatory activity and genomic stability. Indeed, an increased rate of unrepaired DNA damage is considered to be one of the 'hallmarks' of aging. Over the last two decades our appreciation of the complexity of GPCR signaling systems has expanded their functional signaling repertoire. One such example of this is the incipient role of GPCRs and GPCR-interacting proteins in DNA damage and repair mechanisms. Emerging data now suggest that GPCRs could function as stress sensors for intracellular damage, e.g., oxidative stress. Given this role of GPCRs in the DNA damage response process, coupled to the effective history of drug targeting of these receptors, this suggests that one important future activity of GPCR therapeutics is the rational control of DNA damage repair systems.

Informatic deconvolution of biased GPCR signaling mechanisms from in vivo pharmacological experimentation.

Ligands possessing different physico-chemical structures productively interact with G protein-coupled receptors generating distinct downstream signaling events due to their abilities to activate/select idiosyncratic receptor entities ('receptorsomes') from the full spectrum of potential receptor partners. We have employed multiple novel informatic approaches to identify and characterize the in vivo transcriptomic signature of an arrestin-signaling biased ligand, [D-Trp(12),Tyr(34)]-bPTH(7-34), acting at the parathyroid hormone type 1 receptor (PTH1R), across six different murine tissues after chronic drug exposure. We are able to demonstrate that [D-Trp(12),Tyr(34)]-bPTH(7-34) elicits a distinctive arrestin-signaling focused transcriptomic response that is more coherently regulated, in an arrestin signaling-dependent manner, across more tissues than that of the pluripotent endogenous PTH1R ligand, hPTH(1-34). This arrestin-focused response signature is strongly linked with the transcriptional regulation of cell growth and development. Our informatic deconvolution of a conserved arrestin-dependent transcriptomic signature from wild type mice demonstrates a conceptual framework within which the in vivo outcomes of biased receptor signaling may be further investigated or predicted.

Amitriptyline improves motor function via enhanced neurotrophin signaling and mitochondrial functions in the murine N171-82Q Huntington disease model.

Huntington disease (HD) is a neurodegenerative disorder characterized by progressive motor impairment and cognitive alterations. Hereditary HD is primarily caused by the expansion of a CAG trinucleotide repeat in the huntingtin (Htt) gene, which results in the production of mutant huntingtin protein (mHTT) with an expanded amino-terminal polyglutamine (poly(Q)) stretch. Besides pathological mHTT aggregation, reduced brain-derived neurotrophic factor (BDNF) levels, impaired neurotrophin signaling, and compromised mitochondrial functions also contribute to the deleterious progressive etiology of HD. As a well tolerated Food and Drug Administration-approved antidepressant, amitriptyline (AMI) has shown efficacy in treating neurodegenerative murine models via potentiation of BDNF levels and amelioration of alterations in neurotrophin signaling pathways. In this study, we observed profound improvements in the motor coordination of AMI-treated N171-82Q HD model mice. The beneficial effects of AMI treatment were associated with its ability to reduce mHTT aggregation, potentiation of the BDNF-TrkB signaling system, and support of mitochondrial integrity and functionality. Our study not only provides preclinical evidence for the therapeutic potency of AMI in treating HD, but it also represents an important example of the usefulness of additional pharmacogenomic profiling of pre-existing drugs for novel therapeutic effects with often intractable pathological scenarios.

Delineation of a conserved arrestin-biased signaling repertoire in vivo.

Biased G protein-coupled receptor agonists engender a restricted repertoire of downstream events from their cognate receptors, permitting them to produce mixed agonist-antagonist effects in vivo. While this opens the possibility of novel therapeutics, it complicates rational drug design, since the in vivo response to a biased agonist cannot be reliably predicted from its in cellula efficacy. We have employed novel informatic approaches to characterize the in vivo transcriptomic signature of the arrestin pathway-selective parathyroid hormone analog [d-Trp(12), Tyr(34)]bovine PTH(7-34) in six different murine tissues after chronic drug exposure. We find that [d-Trp(12), Tyr(34)]bovine PTH(7-34) elicits a distinctive arrestin-signaling focused transcriptomic response that is more coherently regulated across tissues than that of the pluripotent agonist, human PTH(1-34). This arrestin-focused network is closely associated with transcriptional control of cell growth and development. Our demonstration of a conserved arrestin-dependent transcriptomic signature suggests a framework within which the in vivo outcomes of arrestin-biased signaling may be generalized.

Toll-like receptors 2 and 4 modulate autonomic control of heart rate and energy metabolism.

Toll-like receptors (TLR) are innate immune receptors typically activated by microbial-associated molecular patterns (MAMPs) during infection or damage-associated molecular patterns (DAMPs) as a result of tissue injury. Recent findings suggest that TLR2 and TLR4 signaling play important roles in developmental and adult neuroplasticity, and in learning and memory. In addition, activation of TLR2 and TLR4 worsens ischemic injury to the heart and brain in animal models of myocardial infarction and stroke. TLR activation is also implicated in thermoregulation and fever in response to infection. However, it is not known whether TLRs participate in the regulation of the sympathetic and/or parasympathetic components of the autonomic nervous system (ANS). Here we provide evidence that TLR2 and TLR4 influence autonomic regulation of heart rate (HR) body temperature and energy metabolism in mice. We show that mice lacking TLR2 or TLR4 exhibit reduced basal HR, which results from an increase of parasympathetic tone. In addition, thermoregulatory responses to stress are altered in TLR2-/- and TLR4-/- mice, and brown fat-dependent thermoregulation is altered in TLR4-/- mice. Moreover, TLR2-/- and TLR4-/- mice consume less food and exhibit a greater mass compared to wild type mice. Collectively, our findings suggest important roles for TLR2 and TLR4 in the ANS regulation of cardiovascular function, thermoregulation, and energy metabolism.

Euglycemic agent-mediated hypothalamic transcriptomic manipulation in the N171-82Q model of Huntington disease is related to their physiological efficacy.

Our aim was to employ novel analytical methods to investigate the therapeutic treatment of the energy regulation dysfunction occurring in a Huntington disease (HD) mouse model. HD is a neurodegenerative disorder that is characterized by progressive motor impairment and cognitive alterations. Changes in neuroendocrine function, body weight, energy metabolism, euglycemia, appetite function, and gut function can also occur. It is likely that the locus of these alterations is the hypothalamus. We determined the effects of three different euglycemic agents on HD progression using standard physiological and transcriptomic signature analyses. N171-82Q HD mice were treated with insulin, Exendin-4, and the newly developed GLP-1-Tf to determine whether these agents could improve energy regulation and delay disease progression. Blood glucose, insulin, metabolic hormone levels, and pancreatic morphology were assessed. Hypothalamic gene transcription, motor coordination, and life span were also determined. The N171-82Q mice exhibited significant alterations in hypothalamic gene transcription signatures and energy metabolism that were ameliorated, to varying degrees, by the different euglycemic agents. Exendin-4 or GLP-1-Tf (but not insulin) treatment also improved pancreatic morphology, motor coordination, and increased life span. Using hypothalamic transcription signature analyses, we found that the physiological efficacy variation of the drugs was evident in the degree of reversal of the hypothalamic HD pathological signature. Euglycemic agents targeting hypothalamic and energy regulation dysfunction in HD could potentially alter disease progression and improve quality of life in HD.

The effect of intermittent energy and carbohydrate restriction v. daily energy restriction on weight loss and metabolic disease risk markers in overweight women.

Intermittent energy restriction may result in greater improvements in insulin sensitivity and weight control than daily energy restriction (DER). We tested two intermittent energy and carbohydrate restriction (IECR) regimens, including one which allowed ad libitum protein and fat (IECR+PF). Overweight women (n 115) aged 20 and 69 years with a family history of breast cancer were randomised to an overall 25 % energy restriction, either as an IECR (2500-2717 kJ/d, < 40 g carbohydrate/d for 2 d/week) or a 25 % DER (approximately 6000 kJ/d for 7 d/week) or an IECR+PF for a 3-month weight-loss period and 1 month of weight maintenance (IECR or IECR+PF for 1 d/week). Insulin resistance reduced with the IECR diets (mean - 0·34 (95% CI - 0·66, - 0·02) units) and the IECR+PF diet (mean - 0·38 (95% CI - 0·75, - 0·01) units). Reductions with the IECR diets were significantly greater compared with the DER diet (mean 0·2 (95% CI - 0·19, 0·66) µU/unit, P= 0·02). Both IECR groups had greater reductions in body fat compared with the DER group (IECR: mean - 3·7 (95% CI - 2·5, - 4·9) kg, P= 0·007; IECR+PF: mean - 3·7 (95% CI - 2·8, - 4·7) kg, P= 0·019; DER: mean - 2·0 (95% CI - 1·0, 3·0) kg). During the weight maintenance phase, 1 d of IECR or IECR+PF per week maintained the reductions in insulin resistance and weight. In the short term, IECR is superior to DER with respect to improved insulin sensitivity and body fat reduction. Longer-term studies into the safety and effectiveness of IECR diets are warranted.

"Control" laboratory rodents are metabolically morbid: why it matters.

Failure to recognize that many standard control rats and mice used in biomedical research are sedentary, obese, glucose intolerant, and on a trajectory to premature death may confound data interpretation and outcomes of human studies. Fundamental aspects of cellular physiology, vulnerability to oxidative stress, inflammation, and associated diseases are among the many biological processes affected by dietary energy intake and exercise. Although overfed sedentary rodents may be reasonable models for the study of obesity in humans, treatments shown to be efficacious in these animal models may prove ineffective or exhibit novel side effects in active, normal-weight subjects.

Stromal factors SDF1α, sFRP1, and VEGFD induce dopaminergic neuron differentiation of human pluripotent stem cells.

Human embryonic stem cell (hESC)-derived dopaminergic (DA) neurons hold potential for treating Parkinson's disease (PD) through cell replacement therapy. Generation of DA neurons from hESCs has been achieved by coculture with the stromal cell line PA6, a source of stromal cell-derived inducing activity (SDIA). However, the factors produced by stromal cells that result in SDIA are largely undefined. We previously reported that medium conditioned by PA6 cells can generate functional DA neurons from NTera2 human embryonal carcinoma stem cells. Here we show that PA6-conditioned medium can induce DA neuronal differentiation in both NTera2 cells and the hESC I6 cell line. To identify the factor(s) responsible for SDIA, we used large-scale microarray analysis of gene expression combined with mass spectrometric analysis of PA6-conditioned medium (CM). The candidate factors, hepatocyte growth factor (HGF), stromal cell-derived factor-1 α (SDF1α), secreted frizzled-related protein 1 (sFRP1), and vascular endothelial growth factor D (VEGFD) were identified, and their concentrations in PA6 CM were established by immunoaffinity capillary electrophoresis. Upon addition of SDF1α, sFRP1, and VEGFD to the culture medium, we observed an increase in the number of cells expressing tyrosine hydroxylase (a marker for DA neurons) and ßIII-tubulin (a marker for immature neurons) in both the NTera2 and I6 cell lines. These results indicate that SDF1α, sFRP1, and VEGFD are major components of SDIA and suggest the potential use of these defined factors to elicit DA differentiation of pluripotent human stem cells for therapeutic intervention in PD.

Cortical gene transcription response patterns to water maze training in aged mice.

BACKGROUND: The hippocampus mediates the acquisition of spatial memory, but the memory trace is eventually transferred to the cortex. We have investigated transcriptional activation of pathways related to cognitive function in the cortex of the aged mouse by analyzing gene expression following water maze training. RESULTS: We identified genes that were differentially responsive in aged mice with accurate spatial performance during probe trials or repeated swimming sessions, relative to home cage conditions. Effective learners exhibited significantly greater activation of several pathways, such as the mitogen-activated protein kinase and insulin receptor signaling pathways, relative to swimmers. The genes encoding activity-related cytoskeletal protein (Arc) and brain-derived neurotrophic factor (BDNF) were upregulated in proficient learners, relative to swimmers and home cage controls, while the gene encoding Rho GTPase activating protein 32 (GRIT) was downregulated. We explored the regulation of Arc, BDNF, and GRIT expression in greater morphological detail using in situ hybridization. Recall during probe trials enhanced Arc expression across multiple cortical regions involved in the cognitive component of water maze learning, while BDNF expression was more homogeneously upregulated across cortical regions involved in the associational and sensorimotor aspects of water maze training. In contrast, levels of GRIT expression were uniformly reduced across all cortical regions examined. CONCLUSIONS: These results suggest that cortical gene transcription is responsive to learning in aged mice that exhibit behavioral proficiency, and support a distributed hypothesis of memory storage across multiple cortical compartments.

Aging-related modifications to G protein-coupled receptor signaling diversity.

Aging is a highly complex molecular process, affecting nearly all tissue systems in humans and is the highest risk factor in developing neurodegenerative disorders such as Alzheimer's and Parkinson's disease, cardiovascular disease and Type 2 diabetes mellitus. The intense complexity of the aging process creates an incentive to develop more specific drugs that attenuate or even reverse some of the features of premature aging. As our current pharmacopeia is dominated by therapeutics that target members of the G protein-coupled receptor (GPCR) superfamily it may be prudent to search for effective anti-aging therapeutics in this fertile domain. Since the first demonstration of GPCR-based ß-arrestin signaling, it has become clear that an enhanced appreciation of GPCR signaling diversity may facilitate the creation of therapeutics with selective signaling activities. Such 'biased' ligand signaling profiles can be effectively investigated using both standard molecular biological techniques as well as high-dimensionality data analyses. Through a more nuanced appreciation of the quantitative nature across the multiple dimensions of signaling bias that drugs possess, researchers may be able to further refine the efficacy of GPCR modulators to impact the complex aberrations that constitute the aging process. Identifying novel effector profiles could expand the effective pharmacopeia and assist in the design of precision medicines. This review discusses potential non-G protein effectors, and specifically their potential therapeutic suitability in aging and age-related disorders.

Transferrin fusion technology: a novel approach to prolonging biological half-life of insulinotropic peptides.

Fusion proteins made up of glucagon-like peptide 1 (GLP-1) and exendin-4 (EX-4) fused to a nonglycosylated form of human transferrin (GLP-1-Tf or EX-4-Tf) were produced and characterized. GLP-1-Tf activated the GLP-1 receptor, was resistant to inactivation by peptidases, and had a half-life of approximately 2 days, compared with 1 to 2 min for native GLP-1. GLP-1-Tf retained the acute, glucose-dependent insulin-secretory properties of native GLP-1 in diabetic animals and had a profound effect on proliferation of pancreatic beta-cells. In addition, Tf and the fusion proteins did not cross the blood-brain-barrier but still reduced food intake after peripheral administration. EX-4-Tf proved to be as effective as EX-4 but had longer lived effects on blood glucose and food intake. This novel transferrin fusion technology could improve the pharmacology of various peptides.

Voluntary exercise and caloric restriction enhance hippocampal dendritic spine density and BDNF levels in diabetic mice.

Diabetes may adversely affect cognitive function, but the underlying mechanisms are unknown. To investigate whether manipulations that enhance neurotrophin levels will also restore neuronal structure and function in diabetes, we examined the effects of wheel running and dietary energy restriction on hippocampal neuron morphology and brain-derived neurotrophic factor (BDNF) levels in db/db mice, a model of insulin resistant diabetes. Running wheel activity, caloric restriction, or the combination of the two treatments increased levels of BDNF in the hippocampus of db/db mice. Enhancement of hippocampal BDNF was accompanied by increases in dendritic spine density on the secondary and tertiary dendrites of dentate granule neurons. These studies suggest that diabetes exerts detrimental effects on hippocampal structure, and that this state can be attenuated by increasing energy expenditure and decreasing energy intake.

Allosteric modulators of g protein-coupled receptors: future therapeutics for complex physiological disorders.

G protein-coupled receptors (GPCRs) are one of the most important classes of proteins in the genome, not only because of their tremendous molecular diversity but because they are the targets of nearly 50% of current pharmacotherapeutics. The majority of these drugs affect GPCR activity by binding to a similar molecular site as the endogenous cognate ligand for the receptor. These "orthosterically" targeted drugs currently dominate the existing pharmacopeia. Over the past two decades, novel opportunities for drug discovery have risen from a greater understanding of the complexity of GPCR signaling. A striking example of this is the appreciation that many GPCRs possess functional allosteric binding sites. Allosteric modulator ligands bind receptor domains topographically distinct from the orthosteric site, altering the biological activity of the orthosteric ligand by changing its binding affinity, functional efficacy, or both. This additional receptor signaling complexity can be embraced and exploited for the next generation of GPCR-targeted therapies. Despite the challenges associated with detecting and quantifying the myriad of possible allosteric effects on GPCR activity, allosteric ligands offer the prospect of engendering a facile stimulus-bias in orthosteric ligand signaling, paving the way for not only receptor-selective but also signaling pathway-selective therapies. Allosteric modulators possess specific advantages when considering the treatment of multifactorial syndromes, such as metabolic diseases or age-related cognitive impairment, because they may not greatly affect neurotransmitter or hormone release patterns, thus maintaining the integrity of complex signaling networks that underlie perception, memory patterns, or neuroendocrinological axes while introducing therapeutically beneficial signal bias.

Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics.

The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.