A metagenomics-based diagnostic approach for central nervous system infections in hospital acute care setting

Economic impact of metagenomic next-generation sequencing versus traditional bacterial culture for postoperative central nervous system infections using a decision analysis mode: study protocol for a randomized controlled trial

IMPORTANCE

Diagnosing and treating postoperative central nervous system infections (PCNSIs) remains challenging due to the low detection rate and time-consuming nature of traditional methods for identifying microorganisms in cerebrospinal fluid. Metagenomic next-generation sequencing (mNGS) technology provides a rapid and comprehensive understanding of microbial composition in PCNSIs by swiftly sequencing and analyzing the microbial genome. The current study aimed to assess the economic impact of using mNGS versus traditional bacterial culture-directed PCNSIs diagnosis and therapy in post-neurosurgical patients from Beijing Tiantan Hospital. mNGS is a relatively expensive test item, and whether it has the corresponding health-economic significance in the clinical application of diagnosing intracranial infection has not been studied clearly. Therefore, the investigators hope to explore the clinical application value of mNGS detection in PCNSIs after neurosurgery.

Next-Generation Sequencing for the Detection of Microbial Agents in Avian Clinical Samples

Direct-targeted next-generation sequencing (tNGS), with its undoubtedly superior diagnostic capacity over real-time PCR (RT-PCR), and direct-non-targeted NGS (ntNGS), with its higher capacity to identify and characterize multiple agents, are both likely to become diagnostic methods of choice in the future. tNGS is a rapid and sensitive method for precise characterization of suspected agents. ntNGS, also known as agnostic diagnosis, does not require a hypothesis and has been used to identify unsuspected infections in clinical samples. Implemented in the form of multiplexed total DNA metagenomics or as total RNA sequencing, the approach produces comprehensive and actionable reports that allow semi-quantitative identification of most of the agents present in respiratory, cloacal, and tissue samples. The diagnostic benefits of the use of direct tNGS and ntNGS are high specificity, compatibility with different types of clinical samples (fresh, frozen, FTA cards, and paraffin-embedded), production of nearly complete infection profiles (viruses, bacteria, fungus, and parasites), production of "semi-quantitative" information, direct agent genotyping, and infectious agent mutational information. The achievements of NGS in terms of diagnosing poultry problems are described here, along with future applications. Multiplexing, development of standard operating procedures, robotics, sequencing kits, automated bioinformatics, cloud computing, and artificial intelligence (AI) are disciplines converging toward the use of this technology for active surveillance in poultry farms. Other advances in human and veterinary NGS sequencing are likely to be adaptable to avian species in the future.

Next-generation sequencing: what are the needs in routine clinical microbiology? A survey among clinicians involved in infectious diseases practice

Background

The translation of Next-Generation Sequencing (NGS) from research to clinical microbiology is increasing rapidly, but its integration into routine clinical care struggles to catch-up. A challenge for clinical laboratories is that the substantial investments made in the required technologies and resources must meet both current and forthcoming needs.

Methods

To get a clinical perspective of these needs, we have sent a survey to infectious diseases clinicians of five hospitals, covering the following topics: NGS knowledge, expected syndromes and patients foreseen to benefit from NGS, and expected impact on antimicrobial prescription.

Results

According to clinicians, benefits of NGS are mostly expected in neurological and respiratory infections diagnostics.

Conclusion

A better dialog between microbiologists and clinicians about hopes and limits of NGS in microbiology may help identifying key investments needed for clinical laboratories, today and tomorrow.

Clinical metagenomics-challenges and future prospects

Infections lacking precise diagnosis are often caused by a rare or uncharacterized pathogen, a combination of pathogens, or a known pathogen carrying undocumented or newly acquired genes. Despite medical advances in infectious disease diagnostics, many patients still experience mortality or long-term consequences due to undiagnosed or misdiagnosed infections. Thus, there is a need for an exhaustive and universal diagnostic strategy to reduce the fraction of undocumented infections. Compared to conventional diagnostics, metagenomic next-generation sequencing (mNGS) is a promising, culture-independent sequencing technology that is sensitive to detecting rare, novel, and unexpected pathogens with no preconception. Despite the fact that several studies and case reports have identified the effectiveness of mNGS in improving clinical diagnosis, there are obvious shortcomings in terms of sensitivity, specificity, costs, standardization of bioinformatic pipelines, and interpretation of findings that limit the integration of mNGS into clinical practice. Therefore, physicians must understand the potential benefits and drawbacks of mNGS when applying it to clinical practice. In this review, we will examine the current accomplishments, efficacy, and restrictions of mNGS in relation to conventional diagnostic methods. Furthermore, we will suggest potential approaches to enhance mNGS to its maximum capacity as a clinical diagnostic tool for identifying severe infections.

UltraSEQ, a Universal Bioinformatic Platform for Information-Based Clinical Metagenomics and Beyond

Applied metagenomics is a powerful emerging capability enabling the untargeted detection of pathogens, and its application in clinical diagnostics promises to alleviate the limitations of current targeted assays. While metagenomics offers a hypothesis-free approach to identify any pathogen, including unculturable and potentially novel pathogens, its application in clinical diagnostics has so far been limited by workflow-specific requirements, computational constraints, and lengthy expert review requirements. To address these challenges, we developed UltraSEQ, a first-of-its-kind accurate and scalable metagenomic bioinformatic tool for potential clinical diagnostics and biosurveillance utility. Here, we present the results of the evaluation of our novel UltraSEQ pipeline using an in silico-synthesized metagenome, mock microbial community data sets, and publicly available clinical data sets from samples of different infection types, including both short-read and long-read sequencing data. Our results show that UltraSEQ successfully detected all expected species across the tree of life in the in silico sample and detected all 10 bacterial and fungal species in the mock microbial community data set. For clinical data sets, even without requiring data set-specific configuration setting changes, background sample subtraction, or prior sample information, UltraSEQ achieved an overall accuracy of 91%. Furthermore, as an initial demonstration with a limited patient sample set, we show UltraSEQ's ability to provide antibiotic resistance and virulence factor genotypes that are consistent with phenotypic results. Taken together, the above-described results demonstrate that the UltraSEQ platform offers a transformative approach for microbial and metagenomic sample characterization, employing a biologically informed detection logic, deep metadata, and a flexible system architecture for the classification and characterization of taxonomic origin, gene function, and user-defined functions, including disease-causing infections. IMPORTANCE Traditional clinical microbiology-based diagnostic tests rely on targeted methods that can detect only one to a few preselected organisms or slow, culture-based methods. Although widely used today, these methods have several limitations, resulting in rates of cases of an unknown etiology of infection of >50% for several disease types. Massive developments in sequencing technologies have made it possible to apply metagenomic methods to clinical diagnostics, but current offerings are limited to a specific disease type or sequencer workflow and/or require laboratory-specific controls. The limitations associated with current clinical metagenomic offerings result from the fact that the backend bioinformatic pipelines are optimized for the specific parameters described above, resulting in an excess of unmaintained, redundant, and niche tools that lack standardization and explainable outputs. In this paper, we demonstrate that UltraSEQ uses a novel, information-based approach that enables accurate, evidence-based predictions for diagnosis as well as the functional characterization of a sample.

Clinical application and drug-use-guidance value of metagenomic next-generation sequencing in central nervous system infection

OBJECTIVE

Timely and precise etiology diagnosis is crucial for optimized medication regimens and better prognosis in central nervous system infections (CNS infections). We aimed to analyze the impact of mNGS tests on the management of patients with CNS infections.

METHODS

We conducted a single-center retrospective cohort study to analyze the value of mNGS in clinical applications. Three hundred sixty-nine patients with a CNS infection diagnosis were enrolled, and their clinical data were collected. CDI and DDI were defined in our study to describe the intensity of drug use in different groups. We used LOH and mRS to evaluate if the application of mNGS can benefit CNS infected patients.

RESULTS

mNGS reported a 91.67% sensitivity in culture-positive patients and an 88.24% specificity compared with the final diagnoses. Patients who participated with the mNGS test had less drug use, both total (58.77 vs. 81.18) and daily (22.6 vs. 28.12, P < 0.1, McNemar) intensity of drug use, and length of hospitalization (23.14 vs. 24.29). Patients with a consciousness grading 1 and 3 had a decrease in CDI (Grade 1, 86.49 vs. 173.37; Grade 3, 48.18 vs. 68.21), DDI (Grade 1, 1.52 vs. 2.72; Grade 3, 2.3 vs. 2.45), and LOH (Grade 1, 32 vs. 40; Grade 3, 21 vs. 23) with the application of mNGS. Patients infected with bacteria in the CNS had a reduced CDI, DDI, and LOH in the mNGS group. This was compared with the TraE group that had 49% of patients altered medication plans, and 24.7% of patients reduced drug intensity four days after mNGS reports. This was because of the reduction of drug types.

CONCLUSION

mNGS showed its high sensitivity and specificity characteristics. mNGS may assist clinicians with more rational medication regimens and reduce the drug intensity for patients. The primary way of achieving this is to reduce the variety of drugs, especially for severe patients and bacterial infections. mNGS has the ability of improving the prognosis of CNS infected patients.

Background Filtering of Clinical Metagenomic Sequencing with a Library Concentration-Normalized Model

Metagenomic next-generation sequencing (mNGS) can accurately detect pathogens in clinical samples. However, wet-lab contamination constrains mNGS analysis and may result in erroneous interpretation of results. Many existing methods rely on large-scale observational microbiome studies and may not be applicable to clinical mNGS tests. By generation of a pretrained profile of common laboratory contaminants, we developed an mNGS noise-filtering model based on the inverse linear relationship between microbial sequencing reads and sample library concentration, named the background elimination and correction by library concentration-normalized (BECLEAN) model. Its efficacy was evaluated with bacteria- and yeast-spiked samples and 28 cerebrospinal fluid (CSF) specimens. The diagnostic accuracy, precision, sensitivity, and specificity of BECLEAN with reference to conventional methods and diagnosis were 92.9%, 86.7%, 100%, and 86.7%, respectively. BECLEAN led to a dramatic reduction of background noise without affecting the true-positive rate and thus can provide a time-saving and convenient tool in various clinical settings. IMPORTANCE Most of the existing methods to remove wet-lab contamination rely on large-scale observational microbiome studies and may not be applicable to clinical mNGS testing in individual cases. In clinical settings, only a handful of samples might be sequenced in a run. The lab-specific microbiome can complicate existing statistical approaches for removing contamination from small-scale clinical metagenomic sequencing data sets; thus, use of a preliminary lab-specific training set is necessary. Our study provides a rapid and accurate background-filtering tool for clinical metagenomic sequencing by generation of a pretrained profile of common laboratory contaminants. Notably, our work demonstrates that the inverse linear relationship between microbial sequencing reads and library concentration can serve to identify true contaminants and evaluate the relative abundance of a taxon in samples by comparing the observed microbial reads to the model-predicted value. Our findings extend the previously published research and demonstrate confirmatory results in clinical settings.

Metagenomic Next-Generation Sequencing in the Diagnosis of Infectious Fever During Myelosuppression Among Pediatric Patients with Hematological and Neoplastic Diseases

Purpose

To analyze the contribution of metagenomic next-generation sequencing (mNGS) in the guidance of clinical treatment and outcomes of infection during myelosuppression among children with hematological and neoplastic diseases.

Patients and Methods

The clinical data and results of mNGS assay of febrile patients suspected of infection were retrospectively collected. The characteristics of pathogenic microorganisms and clinical course of myelosuppressed children with hematological diseases were summarized.

Results

Our study included 70 patients (45 males) with a median age of 5 years (range: 0.5 to 13 y). During the study period, there were 96 events of suspected infection. According to comprehensive clinical diagnosis, 73 blood infections, 43 pneumonia and 2 urinary tract infections occurred. The positive rate of mNGS was significantly higher than that of traditional microbial detection (83.3% vs 17.7%). The main pathogens detected by mNGS were Pseudomonas aeruginosa, Acinetobacter, human herpesvirus, Candida and Aspergillus. The average duration of fever was 4.9 days and 11.6 days (P < 0.05), and the average cost of anti-infection treatment was RMB ¥28,077 and 39,898 (P < 0.05) among children received mNGS within 48 hours and more than 48 hours after the onset of infection symptoms.

Conclusion

mNGS contributes to clinical management of children with infection during myelosuppression, especially among patients with negative traditional microbial detection. Early implementation of mNGS in children with symptoms has a tendency to reduce the time of infection, fever and the cost of treatment.

Metagenomics next-generation sequencing for the diagnosis of central nervous system infection: A systematic review and meta-analysis

Objective

It is widely acknowledged that central nervous system (CNS) infection is a serious infectious disease accompanied by various complications. However, the accuracy of current detection methods is limited, leading to delayed diagnosis and treatment. In recent years, metagenomic next-generation sequencing (mNGS) has been increasingly adopted to improve the diagnostic yield. The present study sought to evaluate the value of mNGS in CNS infection diagnosis.

Methods

Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2022 guidelines, we searched relevant articles published in seven databases, including PubMed, Web of Science, and Cochrane Library, published from January 2014 to January 2022. High-quality articles related to mNGS applications in the CNS infection diagnosis were included. The comparison between mNGS and the gold standard of CNS infection, such as culture, PCR or serology, and microscopy, was conducted to obtain true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values, which were extracted for sensitivity and specificity calculation.

Results

A total of 272 related studies were retrieved and strictly selected according to the inclusion and exclusion criteria. Finally, 12 studies were included for meta-analysis and the pooled sensitivity was 77% (95% CI: 70–82%, I² = 39.69%) and specificity was 96% (95% CI: 93–98%, I² = 72.07%). Although no significant heterogeneity in sensitivity was observed, a sub-group analysis was conducted based on the pathogen, region, age, and sample pretreatment method to ascertain potential confounders. The area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) of mNGS for CNS infection was 0.91 (95% CI: 0.88–0.93). Besides, Deek's Funnel Plot Asymmetry Test indicated no publication bias in the included studies (Figure 3, p > 0.05).

Conclusion

Overall, mNGS exhibits good sensitivity and specificity for diagnosing CNS infection and diagnostic performance during clinical application by assisting in identifying the pathogen. However, the efficacy remains inconsistent, warranting subsequent studies for further performance improvement during its clinical application.

Study registration number

INPLASY202120002

Diagnosis of pathogens causing bacterial meningitis using Nanopore sequencing in a resource-limited setting

Aim

The aim of the present study is to compare the performance of 16S rRNA Nanopore sequencing and conventional culture in detecting infectious pathogens in patients with suspected meningitis in a resource-limited setting without extensive bioinformatics expertise.

Methods

DNA was isolated from the cerebrospinal fluid (CSF) of 30 patients with suspected bacterial meningitis. The isolated DNA was subjected to 16S sequencing using MinION™. The data were analysed in real time via the EPI2ME cloud platform. The Nanopore sequencing was done in parallel to routine microbiological diagnostics.

Results

Nanopore sequencing detected bacterial pathogens to species level in 13 of 30 (43%) samples. CSF culture showed 40% (12/30) positivity. In 21 of 30 patients (70%) with suspected bacterial meningitis, both methods yielded concordant results. About nine of 30 samples showed discordant results, of these five were false positive and four were false negative. In five of the culture negative results, nanopore sequencing was able to detect pathogen genome, due to the higher sensitivity of the molecular diagnostics. In two other samples, the CSF culture revealed Cryptococcus neoformans and Streptococcus pneumoniae, which were not detected by Nanopore sequencing. Overall, using both the cultures and 16S Nanopore sequencing, positivity rate increased from 40% (12/30) to 57% (17/30).

Conclusion

Next-generation sequencing could detect pathogens within six hours and could become an important tool for both pathogen screening and surveillance in low- and middle-income countries (LMICs) that do not have direct access to extensive bioinformatics expertise.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12941-022-00530-6.

The Cost of Suspected and Confirmed Bacterial Meningitis Cases Treated at Jimma University Medical Center, Ethiopia

Background

Infections of the central nervous system (CNS) such as meningitis or encephalitis can be caused by myriad of microorganisms and may be life-threatening. In Ethiopia, it is an important cause of premature death and disability, being the 9^th most common cause of years of life lost and loss of disability-adjusted life years.

The objective of this study was to estimate the cost of suspected and confirmed bacterial meningitis among inpatient managed patients at JUMC.

Methods

A facility-based cross-sectional study was conducted from July 28 to September 12, 2018. A semi-structured questionnaire was used in this study. Checklists were used to collect the types of laboratory tests performed and prescribed medications. This cost of illness study was conducted from the patient perspectives. We employed a micro-costing bottom-up approach to estimate the direct cost of meningitis. The human capital approach was used for estimating wages lost.

Result

Among total patients admitted and treated in JUMC, higher proportions (69.8%) were suspected bacterial meningitis but have been treated as confirmed cases. Total median costs for both suspected and confirmed bacterial meningitis patients were estimated to be ETB 98,812.32 (US $ 3,593.2; IQR 1,303.0 to 5,734.0). Total median direct cost was ETB 79,248.02 (US $ 2,881.75; IQR 890.7 to 3,576.7). Moreover, 45.3% of the patients reported that they were either admitted or given medication at JUMC or nearby health facility before their current admissions.

Conclusion

These findings indicate that most cases of bacterial meningitis were treated only empirically, and the cost of the treatment was high, especially for resource-limited countries like Ethiopia. To minimize the burden of meningitis and avoid unnecessary hospitalizations, the availability of diagnostic techniques is vitally important.

Complete Genome Sequencing of Tick-Borne Encephalitis Virus Directly from Clinical Samples: Comparison of Shotgun Metagenomic and Targeted Amplicon-Based Sequencing

The clinical presentation of tick-borne encephalitis virus (TBEV) infection varies from asymptomatic to severe meningoencephalitis or meningoencephalomyelitis. The TBEV subtype has been suggested as one of the most important risk factors for disease severity, but TBEV genetic characterization is difficult. Infection is usually diagnosed in the post-viremic phase, and so relevant clinical samples of TBEV are extremely rare and, when present, are associated with low viral loads. To date, only two complete TBEV genomes sequenced directly from patient clinical samples are publicly available. The aim of this study was to develop novel protocols for the direct sequencing of the TBEV genome, enabling studies of viral genetic determinants that influence disease severity. We developed a novel oligonucleotide primer scheme for amplification of the complete TBEV genome. The primer set was tested on 21 clinical samples with various viral loads and collected over a 15-year period using the two most common sequencing platforms. The amplicon-based strategy was compared to direct shotgun sequencing. Using the novel primer set, we successfully obtained nearly complete TBEV genomes (>90% of genome) from all clinical samples, including those with extremely low viral loads. Comparison of consensus sequences of the TBEV genome generated using the novel amplicon-based strategy and shotgun sequencing showed no difference. We conclude that the novel primer set is a powerful tool for future studies on genetic determinants of TBEV that influence disease severity and will lead to a better understanding of TBE pathogenesis.

Canine Meningoencephalitis of Unknown Origin-The Search for Infectious Agents in the Cerebrospinal Fluid via Deep Sequencing

Meningoencephalitis of unknown origin (MUO) describes a group of meningoencephalitides in dogs with a hitherto unknown trigger. An infectious agent has been suggested as one possible trigger of MUO but has not been proven so far. A relatively new method to screen for viral RNA or DNA is next-generation sequencing (NGS) or deep sequencing. In this study, a metagenomics analysis of the virome in a sample is analyzed and scanned for known or unknown viruses. We examined fresh-frozen CSF of 6 dogs with MUO via NGS using a modified sequence-independent, single-primer amplification protocol to detect a possible infectious trigger. Analysis of sequencing reads obtained from the six CSF samples showed no evidence of a virus infection. The inability to detect a viral trigger which could be implicated in the development of MUO in the examined population of European dogs, suggests that the current techniques are not sufficiently sensitive to identify a possible virus infection, that the virus is already eliminated at the time-point of disease outbreak, the trigger might be non-infectious or that there is no external trigger responsible for initiating MUO in dogs.

Metagenomic Next-Generation Sequencing for Diagnosis of Pediatric Meningitis and Encephalitis: A Review

Metagenomic next-generation sequencing is a novel diagnostic test with the potential to revolutionize the diagnosis of pediatric meningitis and encephalitis through unbiased detection of bacteria, viruses, parasites, and fungi in cerebrospinal fluid. Current literature is mostly observational with variable indications, populations, and timing of testing with resulting variability in diagnostic yield and clinical impact. Diagnostic stewardship strategies are needed to direct testing toward high-impact pediatric populations, to optimize timing of testing, to ensure appropriate interpretation of results, and to guide prompt optimization of antimicrobials. This review highlights the high clinical potential of this test, though future studies are needed to gather clinical impact and cost-effectiveness data for specific indications in pediatric populations.

Viruses Causing Aseptic Meningitis: A Tertiary Medical Center Experience With a Multiplex PCR Assay

Background: Central nervous system (CNS) infection is associated with high rates of morbidity and mortality, and despite advancements in molecular testing, aseptic meningitis remains challenging to diagnose. Aseptic meningitis cases are often underreported worldwide, which impacts the quality of patient care. Therefore, we aimed to assess the results of BioFire® FilmArray® meningitis/encephalitis (ME) PCR panel, clinical characteristics, and etiologies of aseptic meningitis patients.

Methods: From January 2018 to January 2020, all pediatric and adult patients in a large tertiary medical center who underwent lumbar puncture and cerebrospinal fluid (CSF) testing by a ME multiplex PCR panel and who fit the aseptic meningitis definition were retrospectively reviewed.

Results: Data were reviewed from 1,607 patients; 240 met the inclusion criteria (54.6% males; 68.8% <4 years of age). The rate of detected viral causes of aseptic meningitis was 40.4%; therefore, 59.6% of the patients remained with unidentified etiology. Among the identified viral meningitis, enterovirus and human herpesvirus 6 (HHV-6) were the most common (25 and 7.9%, respectively). The median length of hospital stay was 6 days, and it was longer in patients with unidentifiable aseptic meningitis (p < 0.0001).

Conclusion: Aseptic meningitis is common among suspected meningitis patients, but most cases remained of unknown etiology. The most common identified viruses were enterovirus followed by HHV-6, and there is predominance in males and the pediatric age group. These results highlight that further research is needed to identify other etiologies and possible additional viral pathogens for aseptic meningitis in the current diagnostic methods.

IDseq-An open source cloud-based pipeline and analysis service for metagenomic pathogen detection and monitoring

BACKGROUND

Metagenomic next-generation sequencing (mNGS) has enabled the rapid, unbiased detection and identification of microbes without pathogen-specific reagents, culturing, or a priori knowledge of the microbial landscape. mNGS data analysis requires a series of computationally intensive processing steps to accurately determine the microbial composition of a sample. Existing mNGS data analysis tools typically require bioinformatics expertise and access to local server-class hardware resources. For many research laboratories, this presents an obstacle, especially in resource-limited environments.

FINDINGS

We present IDseq, an open source cloud-based metagenomics pipeline and service for global pathogen detection and monitoring (https://idseq.net). The IDseq Portal accepts raw mNGS data, performs host and quality filtration steps, then executes an assembly-based alignment pipeline, which results in the assignment of reads and contigs to taxonomic categories. The taxonomic relative abundances are reported and visualized in an easy-to-use web application to facilitate data interpretation and hypothesis generation. Furthermore, IDseq supports environmental background model generation and automatic internal spike-in control recognition, providing statistics that are critical for data interpretation. IDseq was designed with the specific intent of detecting novel pathogens. Here, we benchmark novel virus detection capability using both synthetically evolved viral sequences and real-world samples, including IDseq analysis of a nasopharyngeal swab sample acquired and processed locally in Cambodia from a tourist from Wuhan, China, infected with the recently emergent SARS-CoV-2.

CONCLUSION

The IDseq Portal reduces the barrier to entry for mNGS data analysis and enables bench scientists, clinicians, and bioinformaticians to gain insight from mNGS datasets for both known and novel pathogens.

Fun(gi)omics: Advanced and Diverse Technologies to Explore Emerging Fungal Pathogens and Define Mechanisms of Antifungal Resistance

The landscape of infectious fungal agents includes previously unidentified or rare pathogens with the potential to cause unprecedented casualties in biodiversity, food security, and human health. The influences of human activity, including the crisis of climate change, along with globalized transport, are underlying factors shaping fungal adaptation to increased temperature and expanded geographical regions. Furthermore, the emergence of novel antifungal-resistant strains linked to excessive use of antifungals (in the clinic) and fungicides (in the field) offers an additional challenge to protect major crop staples and control dangerous fungal outbreaks.

The landscape of infectious fungal agents includes previously unidentified or rare pathogens with the potential to cause unprecedented casualties in biodiversity, food security, and human health. The influences of human activity, including the crisis of climate change, along with globalized transport, are underlying factors shaping fungal adaptation to increased temperature and expanded geographical regions. Furthermore, the emergence of novel antifungal-resistant strains linked to excessive use of antifungals (in the clinic) and fungicides (in the field) offers an additional challenge to protect major crop staples and control dangerous fungal outbreaks. Hence, the alarming frequency of fungal infections in medical and agricultural settings requires effective research to understand the virulent nature of fungal pathogens and improve the outcome of infection in susceptible hosts. Mycology-driven research has benefited from a contemporary and unified approach of omics technology, deepening the biological, biochemical, and biophysical understanding of these emerging fungal pathogens. Here, we review the current state-of-the-art multi-omics technologies, explore the power of data integration strategies, and highlight discovery-based revelations of globally important and taxonomically diverse fungal pathogens. This information provides new insight for emerging pathogens through an in-depth understanding of well-characterized fungi and provides alternative therapeutic strategies defined through novel findings of virulence, adaptation, and resistance.