Potential of multiomics technology in precision medicine

Microbiota-brain axis: Exploring the role of gut microbiota in psychiatric disorders - A comprehensive review

Mental illness is a hidden epidemic in modern science that has gradually spread worldwide. According to estimates from the World Health Organization (WHO), approximately 10% of the world's population suffers from various mental diseases each year. Worldwide, financial and health burdens on society are increasing annually. Therefore, understanding the different factors that can influence mental illness is required to formulate novel and effective treatments and interventions to combat mental illness. Gut microbiota, consisting of diverse microbial communities residing in the gastrointestinal tract, exert profound effects on the central nervous system through the gut-brain axis. The gut-brain axis serves as a conduit for bidirectional communication between the two systems, enabling the gut microbiota to affect emotional and cognitive functions. Dysbiosis, or an imbalance in the gut microbiota, is associated with an increased susceptibility to mental health disorders and psychiatric illnesses. Gut microbiota is one of the most diverse and abundant groups of microbes that have been found to interact with the central nervous system and play important physiological functions in the human gut, thus greatly affecting the development of mental illnesses. The interaction between gut microbiota and mental health-related illnesses is a multifaceted and promising field of study. This review explores the mechanisms by which gut microbiota influences mental health, encompassing the modulation of neurotransmitter production, neuroinflammation, and integrity of the gut barrier. In addition, it emphasizes a thorough understanding of how the gut microbiome affects various psychiatric conditions.

Extremely small and incredibly close: Gut microbes as modulators of inflammation and targets for therapeutic intervention

Chronic inflammation is a hallmark for a variety of disorders and is at least partially responsible for disease progression and poor patient health. In recent years, the microbiota inhabiting the human gut has been associated with not only intestinal inflammatory diseases but also those that affect the brain, liver, lungs, and joints. Despite a strong correlation between specific microbial signatures and inflammation, whether or not these microbes are disease markers or disease drivers is still a matter of debate. In this review, we discuss what is known about the molecular mechanisms by which the gut microbiota can modulate inflammation, both in the intestine and beyond. We identify the current gaps in our knowledge of biological mechanisms, discuss how these gaps have likely contributed to the uncertain outcome of fecal microbiota transplantation and probiotic clinical trials, and suggest how both mechanistic insight and -omics-based approaches can better inform study design and therapeutic intervention.

Gut Microbiota Ecology and Inferred Functions in Children With ASD Compared to Neurotypical Subjects

Autism spectrum disorders (ASDs) is a multifactorial neurodevelopmental disorder. The communication between the gastrointestinal (GI) tract and the central nervous system seems driven by gut microbiota (GM). Herein, we provide GM profiling, considering GI functional symptoms, neurological impairment, and dietary habits. Forty-one and 35 fecal samples collected from ASD and neurotypical children (CTRLs), respectively, (age range, 3–15 years) were analyzed by 16S targeted-metagenomics (the V3–V4 region) and inflammation and permeability markers (i.e., sIgA, zonulin lysozyme), and then correlated with subjects’ metadata. Our ASD cohort was characterized as follows: 30/41 (73%) with GI functional symptoms; 24/41 (58%) picky eaters (PEs), with one or more dietary needs, including 10/41 (24%) with food selectivity (FS); 36/41 (88%) presenting high and medium autism severity symptoms (HMASSs). Among the cohort with GI symptoms, 28/30 (93%) showed HMASSs, 17/30 (57%) were picky eaters and only 8/30 (27%) with food selectivity. The remaining 11/41 (27%) ASDs without GI symptoms that were characterized by HMASS for 8/11 (72%) and 7/11 (63%) were picky eaters. GM ecology was investigated for the overall ASD cohort versus CTRLs; ASDs with GI and without GI, respectively, versus CTRLs; ASD with GI versus ASD without GI; ASDs with HMASS versus low ASSs; PEs versus no-PEs; and FS versus absence of FS. In particular, the GM of ASDs, compared to CTRLs, was characterized by the increase of Proteobacteria, Bacteroidetes, Rikenellaceae, Pasteurellaceae, Klebsiella, Bacteroides, Roseburia, Lactobacillus, Prevotella, Sutterella, Staphylococcus, and Haemophilus. Moreover, Sutterella, Roseburia and Fusobacterium were associated to ASD with GI symptoms compared to CTRLs. Interestingly, ASD with GI symptoms showed higher value of zonulin and lower levels of lysozyme, which were also characterized by differentially expressed predicted functional pathways. Multiple machine learning models classified correctly 80% overall ASDs, compared with CTRLs, based on Bacteroides, Lactobacillus, Prevotella, Staphylococcus, Sutterella, and Haemophilus features. In conclusion, in our patient cohort, regardless of the evaluation of many factors potentially modulating the GM profile, the major phenotypic determinant affecting the GM was represented by GI hallmarks and patients’ age.

The microbiota-gut-brain axis: pathways to better brain health. Perspectives on what we know, what we need to investigate and how to put knowledge into practice

The gut and brain link via various metabolic and signalling pathways, each with the potential to influence mental, brain and cognitive health. Over the past decade, the involvement of the gut microbiota in gut-brain communication has become the focus of increased scientific interest, establishing the microbiota-gut-brain axis as a field of research. There is a growing number of association studies exploring the gut microbiota's possible role in memory, learning, anxiety, stress, neurodevelopmental and neurodegenerative disorders. Consequently, attention is now turning to how the microbiota can become the target of nutritional and therapeutic strategies for improved brain health and well-being. However, while such strategies that target the gut microbiota to influence brain health and function are currently under development with varying levels of success, still very little is yet known about the triggers and mechanisms underlying the gut microbiota's apparent influence on cognitive or brain function and most evidence comes from pre-clinical studies rather than well controlled clinical trials/investigations. Filling the knowledge gaps requires establishing a standardised methodology for human studies, including strong guidance for specific focus areas of the microbiota-gut-brain axis, the need for more extensive biological sample analyses, and identification of relevant biomarkers. Other urgent requirements are new advanced models for in vitro and in vivo studies of relevant mechanisms, and a greater focus on omics technologies with supporting bioinformatics resources (training, tools) to efficiently translate study findings, as well as the identification of relevant targets in study populations. The key to building a validated evidence base rely on increasing knowledge sharing and multi-disciplinary collaborations, along with continued public-private funding support. This will allow microbiota-gut-brain axis research to move to its next phase so we can identify realistic opportunities to modulate the microbiota for better brain health.

Toward Overcoming Treatment Failure in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disorder characterized by inflammation and bone erosion. The exact mechanism of RA is still unknown, but various immune cytokines, signaling pathways and effector cells are involved. Disease-modifying antirheumatic drugs (DMARDs) are commonly used in RA treatment and classified into different categories. Nevertheless, RA treatment is based on a "trial-and-error" approach, and a substantial proportion of patients show failed therapy for each DMARD. Over the past decades, great efforts have been made to overcome treatment failure, including identification of biomarkers, exploration of the reasons for loss of efficacy, development of sequential or combinational DMARDs strategies and approval of new DMARDs. Here, we summarize these efforts, which would provide valuable insights for accurate RA clinical medication. While gratifying, researchers realize that these efforts are still far from enough to recommend specific DMARDs for individual patients. Precision medicine is an emerging medical model that proposes a highly individualized and tailored approach for disease management. In this review, we also discuss the potential of precision medicine for overcoming RA treatment failure, with the introduction of various cutting-edge technologies and big data.

COVID-19 challenges: From SARS-CoV-2 infection to effective point-of-care diagnosis by electrochemical biosensing platforms

In January 2020, the World Health Organization (WHO) identified a new zoonotic virus, SARS-CoV-2, responsible for causing the COVID-19 (coronavirus disease 2019). Since then, there has been a collaborative trend between the scientific community and industry. Multidisciplinary research networks try to understand the whole SARS-CoV-2 pathophysiology and its relationship with the different grades of severity presented by COVID-19. The scientific community has gathered all the data in the quickly developed vaccines that offer a protective effect for all variants of the virus and promote new diagnostic alternatives able to have a high standard of efficiency, added to shorter response analysis time and portability. The industry enters in the context of accelerating the path taken by science until obtaining the final product. In this review, we show the principal diagnostic methods developed during the COVID-19 pandemic. However, when we observe the diagnostic tools section of an efficient infection outbreak containment report and the features required for such tools, we could observe a highlight of electrochemical biosensing platforms. Such devices present a high standard of analytical performance, are low-cost tools, easy to handle and interpret, and can be used in the most remote and low-resource regions. Therefore, probably, they are the ideal point-of-care diagnostic tools for pandemic scenarios.

Clinical Parasitology and Parasitome Maps as Old and New Tools to Improve Clinical Microbiomics

A growing body of evidence shows that dysbiotic gut microbiota may correlate with a wide range of disorders; hence, the clinical use of microbiota maps and fecal microbiota transplantation (FMT) can be exploited in the clinic of some infectious diseases. Through direct or indirect ecological and functional competition, FMT may stimulate decolonization of pathogens or opportunistic pathogens, modulating immune response and colonic inflammation, and restoring intestinal homeostasis, which reduces host damage. Herein, we discuss how diagnostic parasitology may contribute to designing clinical metagenomic pipelines and FMT programs, especially in pediatric subjects. The consequences of more specialized diagnostics in the context of gut microbiota communities may improve the clinical parasitology and extend its applications to the prevention and treatment of several communicable and even noncommunicable disorders.

Dysbiosis, Host Metabolism, and Non-communicable Diseases: Trialogue in the Inborn Errors of Metabolism

Inborn errors of metabolism (IEMs) represent a complex system model, in need of a shift of approach exploring the main factors mediating the regulation of the system, internal or external and overcoming the traditional concept of biochemical and genetic defects. In this context, among the established factors influencing the metabolic flux, i.e., diet, lifestyle, antibiotics, xenobiotics, infectious agents, also the individual gut microbiota should be considered. A healthy gut microbiota contributes in maintaining human health by providing unique metabolic functions to the human host. Many patients with IEMs are on special diets, the main treatment for these diseases. Hence, IEMs represent a good model to evaluate how specific dietary patterns, in terms of macronutrients composition and quality of nutrients, can be related to a characteristic microbiota associated with a specific clinical phenotype (“enterophenotype”). In the present review, we aim at reporting the possible links existing between dysbiosis, a condition reported in IEMs patients, and a pro-inflammatory status, through an altered “gut-liver” cross-talk network and a major oxidative stress, with a repercussion on the health status of the patient, increasing the risk of non-communicable diseases (NCDs). On this basis, more attention should be paid to the nutritional status assessment and the clinical and biochemical signs of possible onset of comorbidities, with the goal of improving the long-term wellbeing in IEMs. A balanced intestinal ecosystem has been shown to positively contribute to patient health and its perturbation may influence the clinical spectrum of individuals with IEMs. For this, reaching eubiosis through the improvement of the quality of dietary products and mixtures, the use of pre-, pro- and postbiotics, could represent both a preventive and therapeutic strategy in these complex diseases.

Inflammation-Associated Microbiota Composition Across Domestic Animals

Domestic animals represent important resources for understanding shared mechanisms underlying complex natural diseases that arise due to both genetic and environmental factors. Intestinal inflammation, particularly inflammatory bowel disease (IBD), is a significant health challenge in humans and domestic animals. While the etiology of IBD is multifactorial, imbalance of symbiotic gut microbiota has been hypothesized to play a central role in disease pathophysiology. Advances in genomic sequencing and analytical pipelines have enabled researchers to decipher the composition of the intestinal microbiota during health and in the context of naturally occurring diseases. This review compiles microbiome genomic data across domestic species and highlights a common occurrence of gut microbiome dysbiosis during idiopathic intestinal inflammation in multiple species, including dogs, cats, horses, cows, and pigs. Current microbiome data obtained from animals with intestinal inflammation are mostly limited to taxonomical analyses in association with broad clinical phenotype. In general, a pathogen or pathosymbiont were not detected. Rather, functional potential of the altered microbiota has been suggested to be one of the key etiologic factors. Among the domestic species studied, canine analyses are currently the most advanced with incorporation of functional profiling of microbiota. Canine IBD parallels features of the disease in humans, thus canines represent a strong natural model for human IBD. While deeper analyses of metagenomic data, coupled with host molecular analyses are needed, comparative studies across domestic species can reveal shared microbial alterations and regulatory mechanisms that will improve our understanding of intestinal inflammation in both animals and humans.

Unmet needs in psoriatic arthritis

Psoriatic arthritis (PsA) is a chronic inflammatory arthritis with a highly variable clinical presentation that does not have a validated molecular or imaging test, making accurate diagnosis a challenge. Consequences of diagnostic delay include irreversible joint damage and significant morbidity. Over the past few decades, there have been major advances in the understanding and treatment of PsA, leading to more targeted therapies. However, there is no current method to predict optimal treatment strategy to achieve minimal disease activity and prevent medication-related adverse events in the management of early disease. PsA is also associated with other comorbidities that include metabolic syndrome and psychosocial burden; two areas that are often unaddressed in the clinical setting and have associated sequelae. This chapter focuses on key domains of unmet needs, which include diagnostic challenges, delay in diagnosis, prognostication systems and stratified medicine approaches and precision medicine strategies for established and emerging therapies.

Cross-correlation of virome-bacteriome-host-metabolome to study respiratory health

A comprehensive understanding of the microbiome-host relationship in respiratory diseases can be elucidated by exploring the landscape of virome-bacteriome-host metabolome data through unsupervised 'multi-omics' approaches. Here, we describe how the composition and function of airway and gut virome and bacteriome may contribute to pathogen establishment and propagation in airway districts and how the virome-bacteriome communities may react to respiratory diseases. A new systems medicine approach, including the characterization of respiratory and gut microbiome, may be crucial to demonstrate the likelihood and odds of respiratory disease pathophysiology, opening new avenues to the discovery of a chain of causation for key bacteria and viruses in disease severity.

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by behavioral alterations and currently affect about 1% of children. Significant genetic factors and mechanisms underline the causation of ASD. Indeed, many affected individuals are diagnosed with chromosomal abnormalities, submicroscopic deletions or duplications, single-gene disorders or variants. However, a range of metabolic abnormalities has been highlighted in many patients, by identifying biofluid metabolome and proteome profiles potentially usable as ASD biomarkers. Indeed, next-generation sequencing and other omics platforms, including proteomics and metabolomics, have uncovered early age disease biomarkers which may lead to novel diagnostic tools and treatment targets that may vary from patient to patient depending on the specific genomic and other omics findings. The progressive identification of new proteins and metabolites acting as biomarker candidates, combined with patient genetic and clinical data and environmental factors, including microbiota, would bring us towards advanced clinical decision support systems (CDSSs) assisted by machine learning models for advanced ASD-personalized medicine. Herein, we will discuss novel computational solutions to evaluate new proteome and metabolome ASD biomarker candidates, in terms of their recurrence in the reviewed literature and laboratory medicine feasibility. Moreover, the way to exploit CDSS, performed by artificial intelligence, is presented as an effective tool to integrate omics data to electronic health/medical records (EHR/EMR), hopefully acting as added value in the near future for the clinical management of ASD.

Interplay between Dietary Polyphenols and Oral and Gut Microbiota in the Development of Colorectal Cancer

Colorectal cancer (CRC) is the third most diagnosed type of cancer worldwide. Dietary features play an important role in its development, and the involvement of human microbial communities in this pathology has also recently been recognized. Individuals with CRC display alterations in gut bacterial composition and a notably higher abundance of putative oral bacteria in colonic tumors. Many experimental studies and preclinical evidence propose that dietary polyphenols have a relevant role in CRC development and progression, mainly attributed to their immunomodulatory activities. Furthermore, polyphenols can modulate oral and gut microbiota, and in turn, intestinal microbes catabolize polyphenols to release metabolites that are often more active and better absorbed than the original phenolic compounds. The current study aimed to review and summarize current knowledge on the role of microbiota and the interactions between dietary polyphenols and microbiota in relation to CRC development. We have highlighted the mechanisms by which dietary polyphenols and/or their microbial metabolites exert their action on the pathogenesis and prevention of CRC as modulators of the composition and/or activity of oral and intestinal microbiota, including novel screening biomarkers and possible nutritional therapeutic implications.

Machine learning in the clinical microbiology laboratory: has the time come for routine practice?

BACKGROUND

Machine learning (ML) allows the analysis of complex and large data sets and has the potential to improve health care. The clinical microbiology laboratory, at the interface of clinical practice and diagnostics, is of special interest for the development of ML systems.

AIMS

This narrative review aims to explore the current use of ML In clinical microbiology.

SOURCES

References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, arXiV, ACM Digital Library and IEEE Xplore Digital Library up to November 2019.

CONTENT

We found 97 ML systems aiming to assist clinical microbiologists. Overall, 82 ML systems (85%) targeted bacterial infections, 11 (11%) parasitic infections, nine (9%) viral infections and three (3%) fungal infections. Forty ML systems (41%) focused on microorganism detection, identification and quantification, 36 (37%) evaluated antimicrobial susceptibility, and 21 (22%) targeted the diagnosis, disease classification and prediction of clinical outcomes. The ML systems used very diverse data sources: 21 (22%) used genomic data of microorganisms, 19 (20%) microbiota data obtained by metagenomic sequencing, 19 (20%) analysed microscopic images, 17 (18%) spectroscopy data, eight (8%) targeted gene sequencing, six (6%) volatile organic compounds, four (4%) photographs of bacterial colonies, four (4%) transcriptome data, three (3%) protein structure, and three (3%) clinical data. Most systems used data from high-income countries (n = 71, 73%) but a significant number used data from low- and middle-income countries (n = 36, 37%). Performance measures were reported for the 97 ML systems, but no article described their use in clinical practice or reported impact on processes or clinical outcomes.

IMPLICATIONS

In clinical microbiology, ML has been used with various data sources and diverse practical applications. The evaluation and implementation processes represent the main gap in existing ML systems, requiring a focus on their interpretability and potential integration into real-world settings.