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Where can I find disease diagnosis datasets?

Where can I find disease diagnosis datasets?


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For an epidemiological study, I'm looking for datasets for any kind of vector-borne disease (i.e. West Nile Virus, Malaria, etc.), or any parasites that are dependent on intermediate hosts (i.e. Schistosoma spp., which have snails as intermediate hosts).

The data should include day or week and location of each diagnosis. I have been able to find diagnosis data for individual countries for each year, but I'm looking for daily or weekly data for city-wide resolution or at least smaller regions inside countries.

Additionally, it does not matter what country/continent the data is for, as long as there are many data points (i.e. at least 10,000). For example, if there is a lot of good data for Brazil, that's fine.

Does anyone know if such information exists, and if so, where I can find it? I've been searching for it for the past month but have had little luck with finding anything useful. Any help and guidance is appreciated!


CDC Wonder, a health database of the US Center of Disease Control and Prevention (CDC), has weekly data for Nationally Notifiable Infectious Diseases and Conditions, United States: Weekly Tables from 1996 to 2020 on the state level for antrax, brucellosis, dengue fever, leptospirosis, malaria, meningococcal disease, Q fever, rabies, etc.

Weekly tables since 2014 are available on Data.CDC.gov.

Weekly tables for 1952-2017 published in the MMWR are available at CDC Stacks MMWR and weekly tables starting in 2018 are available at CDC Stacks NNDSS.

HealthData.gov has two weekly datasets for malaria by US states - here is one.

Open Government Data (OGD) Platform India has data on a district or state level, for example, for malaria.

Keywords that can help in further search:

  • infectious disease
  • week or weekly report or data
  • MMWR (Morbidity and Mortality Weekly Report)
  • surveillance

Keywords, like diagnosis or outbreak, can limit your search too much.


11 Open Source Datasets That Can Be Used For Health Science Projects

Machine learning is now widely deployed across various health sectors because of its ability to make real-time predictions and draw insights which usually go unnoticed given the voluminous and unstructured nature of the datasets. Here are few repositories that have culminated over the years thanks to the never-ending efforts of the researchers to make crucial metadata available to the common public so that they can try them out on their own models:

WHO (World Health Organisation)

WHO’s is authentic as it can it get when it comes to keeping track of the health of all the nations. Its open data source contains categories which include child nutrition, neglected diseases, risk factors pertaining to certain diseases among others.

The data is available in Excel format.

OGD Platform India

This website consists of all the data collected from Indian health agencies and other entities. The categories in the catalogue range from primary health in tribal regions to state wise health reports.

There is an option to search the keyword to avail numerous well-curated resources.

Kaggle- Health Analytics

The dataset consists of 26 indicators like acute illness, chronic illness, immunisation, mortality and others. These indicators, in turn, have sub-categories which cover all the attributes.

The survey was conducted in Empowered Action Group (EAG) states Uttarakhand, Rajasthan, Uttar Pradesh, Bihar, Jharkhand, Odisha, Chhattisgarh and Madhya Pradesh and Assam.

This dataset covers 21 million population and 4.32 million households spread across the rural and urban area of these 9 states.

These benchmarks would help in better and holistic understanding and timely monitoring of various determinants on well-being and health of population particularly Reproductive and Child Health.

Heart Disease Data Set

This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers. The “goal” field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4. Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from absence (value 0).

Brainomics

Project Brainomics provides the technical foundation for this database, based on a semantic web framework, bringing together imaging, genetics and questionnaire data.

OpenfMRI

OpenfMRI.org is a project dedicated to the free and open sharing of raw magnetic resonance imaging (MRI) datasets.

Number of currently available datasets: 95

Number of subjects across all datasets: 3,372

Mental Disorders

This data was collected via Collaborative Psychiatric Epidemiology Surveys (CPES) which were initiated in recognition of the need for contemporary, comprehensive epidemiological data regarding the distributions, correlates and risk factors of mental disorders.

The objective of the CPES was to collect data about the prevalence of mental disorders, impairments associated with these disorders, and their treatment patterns from representative samples of majority and minority adult populations in the United States.

Pima-indians-diabetes

This dataset describes the medical records for Pima Indians and whether or not each patient will have an onset of diabetes

Fields description follow:

preg = Number of times pregnant

plas = Plasma glucose concentration a 2 hours in an oral glucose tolerance test

pres = Diastolic blood pressure (mm Hg)

The Largest CAD Dataset Released With 15M Designs

skin = Triceps skin fold thickness (mm)

test = 2-Hour serum insulin (mu U/ml)

mass = Body mass index (weight in kg/(height in m)^2)

pedi = Diabetes pedigree function

class = Class variable (1:tested positive for diabetes, 0: tested negative for diabetes)

CT Medical Images

The dataset is designed to allow for different methods to be tested for examining the trends in CT image data associated with using contrast and patient age. The data are a tiny subset of images from the cancer imaging archive.

Malaria Datasets

A repository of segmented cells from the thin blood smear slide images from the Malaria Screener research activity.

The dataset contains a total of 27,558 cell images with equal instances of parasitised and uninfected cells.

Mental Health in Tech Survey

This data was collected with an aim to measure mental health in the tech workplace and examine the frequency of mental health disorders among tech workers.

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Achondrogenesis

Infants with achondrogenesis have very short, underdeveloped arms and legs (micromelia), and a short trunk. The ribs, spine and skull are underdeveloped and poorly ossified, meaning that they are not hard like regular bone. The small rib cage leads to poorly formed lungs, and the chest appears small. Infants with achondrogenesis have relatively large stomachs and heads when compared to the rest of their body. [1] [2]

All three types of achondrogenesis have similar features, and it can be difficult to tell the types apart based only on signs and symptoms. In general, infants with type 1A are more likely to have rib fractures, infants with type 1B may have short fingers and toes, and infants with type 2 have very soft hip bones and spinal column. [4]

This table lists symptoms that people with this disease may have. For most diseases, symptoms will vary from person to person. People with the same disease may not have all the symptoms listed. This information comes from a database called the Human Phenotype Ontology (HPO) . The HPO collects information on symptoms that have been described in medical resources. The HPO is updated regularly. Use the HPO ID to access more in-depth information about a symptom.


Research Research

Research helps us better understand diseases and can lead to advances in diagnosis and treatment. This section provides resources to help you learn about medical research and ways to get involved.

Patient Registry

  • A registry supports research by collecting of information about patients that share something in common, such as being diagnosed with Y chromosome infertility. The type of data collected can vary from registry to registry and is based on the goals and purpose of that registry. Some registries collect contact information while others collect more detailed medical information. Learn more about registries.


2. GENOMICS STRUCTURE AND BIOLOGICAL FEATURES OF SARS𠄌OV𠄂

Coronaviruses belong to the order Nidovirales in the family coronaviridae. Coronavirinae and Torovirinae subfamilies are divided from the family. The subfamily Coronavirinae is further divided into four genera: Alpha‐, Beta‐, Gamma‐ and Deltacoronavirus. 15 Phylogenic analysis revealed that SARS𠄌oV𠄂 is closely related to the beta𠄌oronaviruses. Similar to other coronaviruses, the genome of SARS𠄌oV𠄂 is positive‐sense single‐stranded RNA [(+) ssRNA] with a 5′�p, 3'‐UTR poly(A) tail. The length of the SARS𠄌oV𠄂 genome is less than 30 kb, in which there are 14 open reading frames (ORFs), encoding non‐structural proteins (NSPs) for virus replication and assembly processes, structural proteins including spike (S), envelope (E), membrane/matrix (M) and nucleocapsid (N), and accessory proteins. 16 , 17 The first ORF contains approximately 65% of the viral genome and translates into either polyprotein pp1a (nsp1�) or pp1ab (nsp1�). Among them, six nsps (NSP3, NSP9, NSP10, NSP12, NSP15 and NSP16) play critical roles in viral replication. Other ORFs encode structural and accessory proteins. 18 , 19 The S protein is a transmembrane protein that facilitates the binding of viral envelop to angiotensin𠄌onverting enzyme 2 (ACE2) receptors expressed on host cell surfaces. Functionally, the spike protein is composed of receptor binding (S1) and cell membrane fusion (S2) subunits. 20 The N protein attaches to the viral genome and is involved in RNA replication, virion formation and immune evasion. The nucleocapsid protein also interacts with the nsp3 and M proteins. 21 The M protein is one of the most abundant and well𠄌onserved proteins in the virion structure. This protein promotes the assembly and budding of viral particles through interaction with N and accessory proteins 3a and 7a. 22 , 23 The E protein is the smallest component in the SARS𠄌oV𠄂 structure that facilitates the production, maturation and release of virions. 18

The most complex component of the coronaviruses genome is the receptor𠄋inding domain (RBD) in the spike protein. 24 , 25 Six RBD amino acids are necessary for attaching to the ACE2 receptor and hosting SARS𠄌oV‐like coronaviruses. According to multiple sequence alignment, they are Y442, L472, N479, D480, T487 and Y4911 in SARS𠄌oV, and L455, F486, Q493, S494, N501 and Y505 in SARS𠄌oV𠄂. 26 Therefore, SARS𠄌oV𠄂 and SARS𠄌oV vary with respect to five of these six residues. The SARS𠄌oV strain genome sequences derived from humans were very close to those in bats. Even so, several differences have been identified between the gene sequences of the S gene and the ORF3 and ORF8 gene sequences that encode the attachment and fusion proteins and replication proteins, respectively. 27 Specific MERS𠄌oV strains derived from camels were shown to be identical to those extracted from humans, with the exception of differences between the genomic regions S, ORF4b and ORF3. 28 In addition, genome sequencing�sed experiments have shown that human MERS𠄌oV strains are phylogenetically linked to those of bats. However, for the S proteins, the species have a similar genome and protein structures. 29 Based on the recombination studies of orf1ab and S encoding genes, the MERS𠄌oV was derived from the interchange of genetic elements between coronaviruses in camels and bats. In comparison, with a 96% overall identification, the primary protease is strongly protected among SARS𠄌oV𠄂 and SARS𠄌oV. 29 , 30 , 31

The ACE2 protein is found in many mammalian body tissues, primarily in the lungs, kidneys, gastrointestinal tract, heart, liver and blood vessels. 32 ACE2 receptors are vital elements in regulating the renin𠄊ngiotensin𠄊ldosterone system pathway. Based on structural experiments and biochemical studies, SARS𠄌oV𠄂 appears to have an RBD that strongly binds to ACE2 receptors of humans, cats, ferrets and other organisms with the homologous receptors. 33

The genome sequencing of SARS𠄌oV𠄂 in January 2020 was shown to be 96% identical to the bat coronavirus (BatCoV) RaTG13 genome and 80% identical to the SARS𠄌oV genome. 34 However, significant differences exist. For example, the protein 8a sequence in the SARS𠄌oV genome is absent in the 2019‐nCoV, and the protein 8b sequence of SARS𠄌oV𠄂 is 37 amino acids longer than that in SARS𠄌oV. 35

Alignment sequence analysis of the CoV genome indicates non‐structural and structural proteins being 60% and 45% identical, respectively, among various types of CoVs. 36 These data show that nsps are more conservative than structural proteins. RNA viruses have a higher mutational load as a result of shorter replication times (Figure  1 ). 36 Based on comparative genomic studies between SARS𠄌oV𠄂 and SARS‐like coronaviruses, there are 380 amino acid substitutions in the nsps genes and 27 mutations in genes encoding the spike protein S of SARS𠄌oV𠄂. These variations might explain the different behavioral patterns of SARS𠄌oV𠄂 compared to SARS𠄌oVs. 8 For example, the primary N501 T mutation in the Spike protein of SARS𠄌oV𠄂 could have significantly increased its binding affinity to ACE2. 37

The schematic genomic structure of coronavirus. (a) COVID�. (b) MERS𠄌oV. (c) SARS𠄌oV. The typical coronavirus genome is a single‐stranded, which is approximately 25� kb. It contains 5' caps and 3'‐UTR tails. (d) Coronavirusencoding structural proteins four structural genes, including spike, envelope, membrane and nucleocapsid genes, as well as accessory proteins (3a, 3b, 6, 7a, 7b, 8b, 9b and ORFs)

2.1. Pathogenesis of SARS𠄌oV𠄂

The entry of the SARS𠄌oV𠄂 into host cells and release their genomes into target cells is dependent on a sequence of steps. The virus uses the protein spike, which is important for assessing tropism and virus transmissibility. Additionally, SARS𠄌oV𠄂 even targets human respiratory epithelial cells with ACE2 receptors, indicating a structure of RBD similar to SARS𠄌oV. 38 After attachment of the S1‐RBD to the ACE2 receptor, host cell‐surface proteases such as TMPRSS2 (transmembrane serine protease 2) act on a critical cleavage site on S2. 38 This results in membrane fusion and viral infection. Following virus entry, the uncoated genomic RNA is translated into polyproteins (pp1a and pp1ab) and then assembled into replication/transcription complexes with virus‐induced double‐membrane vesicles (DMVs). Subsequently, this complex replicates and synthesizes a nested set of subgenomic RNA by genome transcription, encoding structural proteins and some accessory proteins. Newly formed virus particles are assembled by mediating the endoplasmic reticulum and the Golgi complex. Finally, virus particles are budded and released into the extracellular milieu compartment. Thus, both the viral replication cycle and progression begin. 10

Inside the host cells, survival of SARS CoVs is maintained by multiple strategies to evade the host immune mechanism, which can also be generalized to SARS𠄌oV𠄂. 39 , 40 As a result of the lack of pathogen𠄊ssociated molecular patterns on DMVs originating from the first step of SARS𠄌oVs infection, they are not recognized by pattern recognition receptors of the host immune system. 25 Nsp1 can impede the interferon (IFN)‐I responses through several mechanisms, such as a silencing of the host translational system, the induction of host mRNA degradation and the repression of transcription factor signal transducer and activator of transcription (STAT)1 phosphorylation. Nsp3 antagonizes interferon and cytokine production by blocking the phosphorylation of interferon regulation factor 3 (IRF3) and interrupting the nuclear factor‐kappa B (NF‐㦫) signaling pathway. NSPs 14 and 16 cooperate to form a viral 5′ cap similar to that of the host. Thus, the viral RNA genome is not recognized by immune system cells. 41 The accessory proteins ORF3b and ORF6 can disrupt the IFN signaling pathway by inhibiting IRF3 and NF‐KB�pendent IFNβ expression and blocking the JAK‐STAT signaling pathway, respectively. Also, IFN signaling is flattened by structural proteins M and N, which result in a disturbance in TANK𠄋inding kinase 1 (TBK1)/IKB kinase ε and TRAF3/6‐TBK1‐IRF3/NF‐㦫/AP1 signals. 22 , 39 Because the D614 G mutation is found in the outer spike protein of the virus, this attracts a huge amount of attention from the human immune system and may therefore impair the ability of SARS𠄌oV𠄂 to avoid vaccine‐induced immunity. D614 G is not in the RBD, although it is involved in the interaction between individual spike protomers that regulate their mature trimeric form on the surface of the virion by hydrogen bonding. 42 Korber et al. reported that the SARS𠄌oV𠄂 variant in the D614 G spike protein has become influential across the globe. Although clinical and in vitro evidence indicate that D614 G alters the phenotype of the virus, the effect of the mutation on replication, pathogenesis, vaccine and therapy development is relatively unknown. 43 From in vitro and clinical evidence, it is apparent that D614 G has a distinct phenotype, although it is not clear whether this is the outcome of verified adaptation to human ACE2, as well as whether it enhances transmissibility, or will have a significant impact. 43

2.2. Diagnosis of COVID�

Early diagnosis and isolation of suspected patients play a vital role in controlling this outbreak. 44 The specificity and sensitivity of different diagnostic techniques differs between populations and the types of equipment employed. 45 Several proceedures have been recommended for the diagnosis of COVID�:

COVID� symptoms are observed approximately 5 days after incubation. 46 The median time of symptom onset from COVID� incubation is 5.1 days, and those infected display symptoms for 11.5 days. 47 This duration was shown to have a close link with the patient's immune system and age. Gastrointestinal symptoms include diarrhea, vomiting and anorexia, recorded in almost 40% of patients. 48 , 49 Up to 10% of patients with gastrointestinal symptoms show no signs of fever or respiratory tract infections. 50 COVID� has also been linked to hypercoagulable disease, elevating the risk of venous thrombosis. 51 There are also records of neurological symptoms (such as fatigue, dizziness and disturbed awareness), ischemic and hemorrhagic strokes, and muscle damage. 52 Many extrapulmonary symptoms comprise skin and eye manifestations. Italian researchers have identified skin manifestations in 20% of patients. 53 The clinical outlook for children can progressively worsen as a result of respiratory failure, which could not be corrected within 1𠄳 days by traditional oxygen (i.e. nasal catheter 54 ) in severe cases the hallmarks are septic shock, sepsis, extreme and continuum bleeding as a result of coagulation abnormalities, and metabolic acidosis. 55

Septic shock could cause severe damage and impair several organs, in addition to a severe pulmonary infection. When extrapulmonary system dysfunction occurs, including the circulatory and the digestive systems, septic shock is probable, and the mortality rate increases substantially. 55 Premature delivery and intrauterine hypoxia occur when the prenate is deprived of an adequate environment of oxygen. Insidious symptoms require specific care in some newborn and preterm infants. Reports have indicated a good prognosis for children within 1 or 2 weeks. 55 Children are prone to a hyperinflammatory response to COVID� similar to Kawasaki disease, which responds well to management, for which a new term is being coined. 56

Also, considerable research has revealed the age distribution of adolescent patients between the ages of 25 to 89 years. Many elderly patients were between 35 and 55 years, and fewer cases among newborns and infants were found. An analysis of the initial transmission dynamics of the virus showed that the median age of patients was 59 years, varying from 15 to 89 years most (59%) were male. 48

Nonspecific screening tests for COVID� in exposed patients

The findings of most blood tests are usually nonspecific but could help determine the causes of the disease. A complete blood count typically shows a normal or low count of white blood cells and lymphopenia. C‐reactive protein (CRP) and erythrocyte sedimentation rate were generally increased, which would optimally be rechecked on days 3, 5 and 7 after admission. 1 , 57 , 58 Creatine kinase plus myoglobin, aspartate aminotransferase and alanine aminotransferase, lactate dehydrogenase, D𠄍imer, and creatine phosphokinase levels could be increased in severe forms of COVID� disease. During viral�terial co‐infections, procalcitonin levels may be elevated. 59 , 60 In a systematic review and meta𠄊nalysis study, Pormohammad et al. 61 investigated the accessible laboratory results obtained among 2361 SARS𠄌oV2 patients, with the results demonstrating 26% leukopenia, 13.3% leukocytosis and 62.5% lymphopenia. Also, among 2200 patients, 91% and 81% revealed elevated platelets (thrombocytosis) and CRP, respectively. 61 Additionally, a review of case studies identified clinical diagnosis and clinical parameter modification in a 47‐year‐old man diagnosed with the disease from Wuweian. 62

To investigate the effect of the coronavirus during the acute phase of the disease, plasma cytokines/chemokines tumor necrosis factor (TNF)‐α and interleukin (IL)𠄁β, IL1RA, IL2, IL4, IL5, IL𠄆, IL�, IL13, IL15 and IL17A were measured. 1 , 63 One study showed that macrophages and dendritic cells play crucial roles in an adaptive immune system. These cells contain inflammatory cytokines and chemokines, such as IL𠄆, IL𠄈, IL�, TNF‐α, monocyte chemoattractant protein𠄁, granulocyte‐macrophage colony‐stimulating factor and granulocyte colony‐stimulating factor. These inflammatory reactions could cause a systemic inflammation. 64

Therefore, fecal and urine tests have been recommended for patients and health staff to detect possible alternate transmission. Consequently, the advancement of tools for determining the different transmission modes, including fecal and urine samples, is urgently warranted to develop strategies for inhibiting and minimizing transmission, as well as develop therapies to control the disease. 65

Chest X‐ray examination may display diverse imaging characteristics or patterns in COVID� patients with a different disease severity and duration. Imaging results differ based on patient age, disease stage during screening, immune competency and drug therapy protocols. 66 On the other hand, computed tomagraphy (CT) imaging is essential for monitoring disease progression and assessing therapeutic effectiveness. It can be re𠄎xamined 1 to 2 days after admission, based on the Diagnostic and Treatment Protocols Regulation (DTPR). 67

The cardinal hallmark of COVID� was multiple, bilateral, posterior and peripheral ground‐glass opacities with or without pulmonary consolidation and, in severe cases, infiltrating shadows. 68 Autopsy analysis of a COVID� patient displayed fluid accumulation and hyaline membrane formation in alveolar walls, which may be the primary pathological driver of the ground‐glass opacity. 69

However, further studies indicated that small patchy shadows, pleural changes, a subpleural curvilinear line and reversed halo signs are generally observed in COVID� patients. 70 , 71 The intralobular lines and thickened interlobular septa were shown in a crazy‐paving pattern on the ground‐glass opacity background. 67 Also, several lobar lesions can be found in the respiratory system in children with a severe infection. Evidence showed that chest CT manifestations (pulmonary edema) reported for COVID� are generally close to SARS and MERS. 69

Evidence has indicated that an initial chest CT has a higher detection rate (approximately 98%) compared to reverse transcriptase‐polymerase chain reaction (RT‐PCR) (approximately 70%) in infected patients. Xie et al. 72 demonstrated that about 3% of patients have no primary positive RT‐PCR but have a positive chest CT therefore, both tests are recommended for COVID� patients. CT of the chest comprises an urgent and simple method for detecting initial COVID� infection with a high sensitivity for prompt diagnosis and disease progression monitoring in patients. Particular notice should be paid to the role of radiologists in finding novel infectious diseases.

The clinical diagnosis of COVID� is focused primarily on epidemiological data, clinical symptoms and some adjuvant technologies, such as nucleic acid detection and immunological assays. In addition, the isolation of SARS𠄌oV𠄂 requires high‐throughput equipment (biosafety level𠄃) to ensure personnel safety. Moreover, serological tests have not yet been validated. In the field of molecular diagnosis, there are three main issues: (i) decreasing the number of false negatives by detecting minimal amounts of viral RNA (ii) avoiding the number of false positives through the correct differentiation of positive signals between different pathogens and (iii) a high capacity for fast and accurate testing of a large number of samples in a short time. 73

2.3. Nucleic acid detection

Two widely used technologies for SARS𠄌oV𠄂 nucleic acid detection are the real‐time RT‐PCR (rRT‐PCR) and high‐throughput sequencing. Nevertheless, as a result of a reliance on equipment and high costs, high‐throughput sequencing in clinical diagnosis is restricted. Access to the whole genome structure of SARS𠄌oV𠄂 has helped the design of specific primers and has introduced the best diagnostic protocols. 47 , 74 In the first published reports on applying the rRT‐PCR in COVID� diagnosis, targeting the spike gene region (S) of SARS𠄌OV𠄂 has shown remarkable specificity and limited sensitivity. 68 Later, the sensitivity of this technique was greatly improved by the use of specific probes for the other viral‐specific genes, including RNA�pendent RNA polymerase (RdRp) in the ORF1ab region, Nucleocapsid (N) and Envelop (E). To avoid cross‐reaction with other human coronaviruses and prevent the potential genetic drift of SARS𠄌oV𠄂, two molecular targets should be involved in this assay: one nonspecific target to detect other CoVs, and one specific target for SARS𠄌oV𠄂. The comparison of the results obtained from targeting all studied genes exhibited that the RdRp gene is the most appropriate target with the highest sensitivity. The RdRp assays were validated in approximately 30 European laboratories using synthetic nucleic acid technology. 73 Currently, Chan et al. 75 have proposed a novel RT‐PCR assay targeting a sequence of the RdRp/Hel that could detect low SARS𠄌oV𠄂 load in the upper respiratory tract, plasma and saliva samples without any cross‐reactivity with other common respiratory viruses. Although the CDC‐recommended assays in the USA rely on two nucleocapsid proteins N1 and N2, the WHO recommends the E gene assay as a first‐line screening, followed by the RdRp gene assay as a confirmatory test. Based on the most recent evidence, the QIAstat𠄍x SARS𠄌oV𠄂 panel, a multiplex RT‐real time PCR system targeting genes RdRp and E, remains highly sensitive despite the nucleotide variations affecting the annealing of the PCR assay. 76

Generally, quantitative (RT‐PCR) RT‐qPCR has high specificity as a gold standard assay for the final diagnosis of COVID�. However, its sensitivity could be variable based on viral load, RNA extraction technique, sampling source and disease stage during the time of sampling. Indeed, the RT‐PCR false‐positive results are related to the cross𠄌ontamination of samples and handling errors. By contrast, inaccuracies during any stage of the collection, storage and processing of samples may lead to false‐negative results. Some studies have revealed that samples from the upper respiratory tract (bottom of the nostrils and the oropharynx) are more desirable for the RT‐PCR assay as a result of many viral copies. 77 Moreover, other shortcomings of RT‐qPCR assays include biological safety hazards arising from maintaining and working on patient samples, as well as time𠄌onsuming and cumbersome nucleic acid detection process. 66 , 68

To improve the molecular diagnostic techniques for COVID�, isothermal amplification�sed methods are currently in development. The loop‐mediated isothermal amplification (LAMP) utilizes the DNA polymerase and 4 to 6 different primers binding to the distinct sequences on the target genome. 78 In the LAMP reactions, the amplified DNA is indicated by turbidity arising from a by‐product of the reaction, a detectable color generated by a pH‐sensitive dye, or fluorescence produced by a fluorescent dye. 79 The approach occurs at a single temperature, in less than 1 hour, and with minimal background signals. The LAMP diagnostic testing for COVID� is more specific and sensitive compared to the conventional RT‐PCR assays and does not dependent on specialized laboratory equipment such as a thermocycler. However, as a result of the multiplicity of primers used in this method, optimizing the reaction conditions presents a major challenge. 80 , 81

2.4. Microarray�sed technique

Microarray is a rapid and high‐throughput method for the COVID� assay. 82 As a brief summary of the protocol, the coronavirus RNA will first produce cDNA labeled with specific probes via reverse transcription. Complementary DNA is produced by coronavirus RNA templates and then through reverse transcription labeling with particular probes. The labeled targets are hybridized to the probe microarray. Free DNAs are removed by washing the solution. Finally, particular probes identify COVID� RNA. 82 Shi et al. 83 successfully performed SARS𠄌oV detection in samples from patients. In their study, Xu et al. 84 investigated a wide range of spike gene polymorphisms with great accuracy. Also, other studies have designed fluorescence and nonfluorescence methods to detect the entire coronavirus genus with promising efficacy. 85 , 86 Jiang et al. 87 constructed a SARS𠄌oV𠄂 proteome microarray consisting of 18 out of 28 expected proteins and administered it to 29 convalescent cases to characterize the immunoglobulin (Ig)G and IgM reactions in the sera. It was revealed that all of these patients had IgM and IgG antibodies, which recognize and bind SARS𠄌oV𠄂 proteins, especially S1and N proteins. In addition to these proteins, important antibody responses to NSP5 and ORF9b are also recognized. The S1‐specific IgG signal relates strongly to age and lactate dehydrogenase lactate dehydrogenase levels and negatively relates to the lymphocyte ratio. Shen et al. progressed the RT‐LAMP experiment to show signals using a quenching probe with the same efficiency as the standard RT‐PCR test with respect to MERS𠄌oV identification. 80

Antigen detection and immunological techniques can be used for a rapid and cost�tive diagnosis at the same time as providing an alternative to molecular methods. Immunological techniques including the immunofluorescence assay, direct fluorescence antibody test, nucleocapsid protein detection assay, protein chip, semiconductor quantum dots and the microneutralization assay define a binding between a viral antigen and a specific antibody. 88 , 89 , 90 , 91 These immunological methods are simple to operate but have low specificity/sensitivity. In the case of COVID�, virus morphology can be observed by electron microscopy according to traditional Koch’s postulates. 92 , 93 Serological tests can improve coronavirus detection such that associated antigens and monoclonal antibodies can represent a new diagnostic approach for future development (Figure  2 ). 94 , 95

Diagnostic protocol recommended for COVID�

Serological tests could be specific to one type of immunoglobulin, they can concurrently measure IgM and IgG antibodies, or they may be absolute antibody examinations, which often measure IgA antibodies. 96 Based on the specific procedure and device, these experiments will usually be carried out within 1𠄲 hours after a sample arrives in the laboratory and is loaded onto the appropriate platform. 97 Guo et al. 98 indicated that IgA and IgM antibodies have positive rates of 93.0% and 85.5% after 3𠄶 days, respectively. Also, 78.0% of positive IgG antibodies were detected during 10� days. The efficiency of detection by an IgM enzyme‐linked immunposorbent assay (ELISA) is higher than that of qPCR after 5.5 days of symptom onset. After 5 days, IgM ELISA detection is more efficient than a qPCR.

Moreover, the combination of PCR and IgM ELISA increased the detection rate by 98.5%. 98 Xiang et al. 99 tested 63 infected patients of SARS𠄌oV𠄂 admitted to Jinyintan Hospital in Wuhan, Hubei, China. Patient serum samples were evaluated using an ELISA and indirect ELISA IgG capture. The study results indicate that IgM was positive with an accuracy of 64.3%, a sensitivity of 44.4% and a specificity of 100% in 28 of 63 samples. The sample identification of 52 cases also showed a positive IgG test with a sensitivity of 82.54%, a specificity of 100% and an accuracy of 88.8%. In addition, a sensitivity of 87.3% was achieved using IgM and IgG combination analysis. 99

Liu et al. evaluated the anti‐IgM and anti‐IgG produced against recombinant spike protein and nucleocapsid protein of SARS𠄌oV𠄂 in 397 PCR confirmed COVID� patients and 128 negative cases at eight distinct clinical sites. The average sensitivity and specificity of the examination was 88.5% and 90.5%, respectively. The findings showed considerable detection consistency among the different types of venous and fingerstick blood samples. Compared to a single IgM or IgG test, the IgM‐IgG combination analysis has a higher effectiveness and sensitivity. 37 , 100 Therefore, it is important and urgent to improve several sensitive and specific supplementary approaches for COVID� diagnosis.

2.5. CRISPR technique

Nucleic acid detection with CRISPR�s13a/C2c2 is a highly rapid, sensitive and specific molecular detection platform, which may aid in the epidemiology, diagnosis and control of the disease. In addition, Cas13a/C2c2 can detect the expression of transcripts in live cells and different diseases. 101 , 102 Zhang et al. presented a protocol for the detection of COVID� using the CRISPR diagnostics�sed SHERLOCK (Specific High Sensitivity Enzymatic Reporter UnLOCKing) technique. RNA fragments of the SARS𠄌oV𠄂 virus help detect target sequences of approximately 100 copies. The experiment is performed by isothermal amplification of the extracted nucleic acid of samples from patients and then amplification of the viral RNA sequence via Cas13 and is finally read out by a paper dipstick in less than 1 hour. 103 , 104 Huang et al. 105 established a CRISPR�sed assay by a custom CRISPR Cas12a/gRNA complex. They used a fluorescent probe to identify target amplicons produced by standard RT‐PCR or isothermal recombinase polymerase amplification. This method showed specific detection at places not equipped with the PCR systems needed for qPCR diagnostic tests in real time. The analysis allows the identification of SARS𠄌oV𠄂 positive samples with a test‐to‐response time of approximately 50 minutes and a detection limit of two copies of each sample to be detected. The findings of the CRISPR test on nasal samples collected from persons with COVID� were comparable with matched data achieved from the CDC𠄊pproved RT‐qPCR test. 105

Broughton et al. 106 described the development of a fast (< 40 min), simple‐to‐implement and precise CRISPR�s12�sed lateral flow test to diagnose SARS𠄌oV𠄂 from RNA extract from a nasal swab. Using artificial reference samples and clinical specimens from patients, comprising patients diagnosed with COVID� disease and 42 patients with other respiratory illnesses, they confirmed their process. This CRISPR�sed approach has provided a visual and quicker alternative option to the SARS𠄌oV𠄂 real‐time RT‐PCR method used in the US Centers for Disease Control and Prevention, with approximately 100% negative predictive agreement and 95% positive predictive agreement. 106

2.6. LAMP�sed technique

Loop‐mediated isothermal amplification (LAMP) is a new isothermal nucleic acid amplification method with great efficiency. This is used to amplify RNAs and DNAs with high specificity and sensitivity as a result of its exponential amplification feature and six particular target sequences diagnosed by four separate primers. 107 The LAMP assay is rapid and does not need high‐priced reagents or equipment. Furthermore, the gel electrophoresis method is widely utilized for investigation of the amplified items to detect endpoints. Hence, the LAMP test will help to decrease the cost of coronavirus detection. Several strategies for the detection of coronavirus based on LAMP are defined here, as developed and performed in clinical diagnosis. 108

Poon et al. 109 have reported a simple LAMP test in the SARS study and demonstrated the feasibility of this method for SARS𠄌oV detection. The SARS𠄌oV ORF1b site was selected for SARS detection and amplified in the presence of six primers via the LAMP reaction, and then the amplified products were assessed by gel electrophoresis. The sensitivity and detection levels in LAMP test for SARS are close to those of traditional PCR�sed techniques. Pyrc et al. 110 effectively applied LAMP to HCoV‐NL63 detection with a desirable sensitivity and specificity in mobile cell cultures and clinical specimens. Particularly, one replica of RNA template was found to be responsible for the detection restriction. Amplification is observed as fluorescent dye or magnesium pyrophosphate precipitation. These techniques can be achieved in real time by monitoring the turbidity of the pyrophosphate or fluorescence, which correctly overcome the restriction of endpoint detection. 110

Shirato et al. 111 developed a beneficial RT‐LAMP assay for the diagnosis and epidemiological monitoring of human MERSCoV. This method was highly specific, without any cross‐reaction with other specific respiratory viruses, and detected as few as 3.4 copies of RNA. 111 Subsequently, they developed the RT‐LAMP assay by revealing a sign using a quenching probe (QProbe), which has the same efficiency as the usual real‐time RT‐PCR test with respect to MERSCoV detection. 112

Based on other evidence, a nucleic acid visualization method was developed that combines RT‐LAMP and a vertical flow visualization strip for MERS detection. 113

2.7. Penn RAMP technology

Based on the effectiveness reported by Zhang et al. 104 using the comparatively less sensitive LAMP, the improved sensitivity of the Penn‐RAMP technique achieved by Huang et al. 114 , which is attributable to an updated two‐step LAMP protocol, can prove to be substantially effective as a diagnostic. To amplify specific targets by recombinase polymerase amplification, in which all targets are simultaneously amplified, the Penn‐RAMP requires a preliminary reaction with outer LAMP primers. A next highly precise LAMP reaction is then triggered. Especially, the first stage uses F3 and B3 outer LAMP primers, whereas the other four RAMP primers are further mixed in the stage 2. Compared to normal LAMP, this ‘nested’ concept considerably improved the sensitivity of LAMP by approximately 10� times, especially when working with distilled and crude samples. 115 Additionally, when extended to mock trials, the Penn‐RAMP methodology was given a 100% approval rating at 7� copies of viral RNA per reaction, compared to a 100% approval rating at the 700 viral RNA copies needed for PCR analysis. 114 , 115

2.8. Droplet digital PCR

For the direct identification and quantification of DNA and RNA targets, droplet digital PCR (ddPCR) comprises an extremely sensitive technique. 116 It has been widely used for infectious disease conditions, particularly because of its ability to identify a few copies of viral genomes accurately and efficiently. 117 If low‐level and/or residual viral existence identification is appropriate, ddPCR quantitative data are much more insightful than those provided by regular RT‐PCR tests. In view of the need to restrict (as far as possible) false‐negative results in COVID� diagnosis, use of the ddPCR can provide a vital support. Even so, the ddPCR assay is still very rarely studied in clinical settings and there is currently no available evidence for European cases. 118

2.9. Next‐generation sequencing (NGS)�sed technique

RNA viruses come in great assortment of varieties, and they are the etiological specialists of numerous significant human and animal infectious diseases. 119

RNA viruses comprise the major variety and are the etiologic agents of very infectious diseases in humans and animals such as SARS, hepatitis, influenza and IB (avian infectious bronchitis). High‐throughput NGS technology has a vital role in primary and accurate diagnosis. 120 In addition, the NGS method can detect whether or not various types of virus comprise a pathogen. The fast novel technique of viruses by NGS, including DNA‐sequencing and RNA‐sequencing has developed the identification of viral diversity. 121 The identification of a huge range of pathogen using NGS technologies is also significant for controlling viral infection caused by a new pathogen. 122 In recent years, the advancement of the NGS method via RNA‐sequencing has enabled us to make great progress in the fast recognition of new RNA viruses. RNAsequencing detects millions of reversely transcribed DNA fragments from complex RNA samples at the same time using random primers. 123 Chen et al. 122 reported a new duck coronavirus using the RNA‐sequencing method, which differed from that of chicken IBV (infectious bronchitis virus). 122 The new duck‐specific CoV was a possible new species within the genus Gamma𠄌oronavirus, as shown by sequences of the viral 1b gene from three regions.

In conclusion, the outbreak of a novel virus emerged at the end of December 2019. COVID� spread immediately and challenged medicine, economics and public health worldwide. Numerous evidence proposed that the ACE2 receptors comprise crucial structural proteins for virus budding and entry into host cells. Both transmission from unidentified intermediate hosts to cross‐species and human to human transmission have been recognized. Hence, early detection and isolation of suspected patients can play an essential role in controlling this outbreak. Currently, diagnostic methods for COVID� are numerous hence, it is imperative to choose a suitable detection protocol. Each of the described techniques has its specific disadvantages and advantages. Both chest CT imaging and RT‐PCR tests are recommended for COVID� patients. However, the use of PCR requires various equipment and a well𠄎stablished laboratory. LAMP can be detected with low numbers of DNA or RNA templates within 1 hour. Microarray is an expensive method for COVID� diagnosis, and other newly developed methods also require additional investigation to achieve rapid development and detection in the future. Given that the number of infected cases is rapidly increasing, future studies should reveal the secrets of the molecular pathways of the virus with respect to developing targeted vaccines and antiviral treatments.


Symptom Checker: Symptoms & Signs A-Z

Find your symptom or sign of disease by using the Symptom Checker.

For a list of symptoms, you can use the symptom checker for men or for women a-z lists.

What is the difference between a symptom and a sign?

A symptom is any subjective evidence of disease, while a sign is any objective evidence of disease. Therefore, a symptom is a phenomenon that is experienced by the individual affected by the disease, while a sign is a phenomenon that can be detected by someone other than the individual affected by the disease. For examples, anxiety, pain, and fatigue are all symptoms. In contrast, a bloody nose is a sign of injured blood vessels in the nose that can be detected by a doctor, a nurse, or another observer.

Health-care professionals use symptoms and signs as clues that can help determine the most likely diagnosis when illness is present. Symptoms and signs are also used to compose a listing of the possible diagnoses. This listing is referred to as the differential diagnosis. The differential diagnosis is the basis from which initial tests are ordered to narrow the possible diagnostic options and choose initial treatments.

Our Symptom Checker for children, men, and women, can be used to handily review a number of possible causes of symptoms that you, friends, or family members may be experiencing. There are many causes for any particular symptom, and the causes revealed in the symptom checker are not exhaustive. That is, they are not intended to be a listing of all possible causes for each symptom but are representative of some of the causes that can be underlying various symptoms.


Molecular Biology of the Huntington’s Disease Genetic Test

Genetic testing can reveal variations in genes that may cause illness or disease. It can be done predictively, to assess a person&rsquos risk of developing a condition, or diagnostically, to confirm a diagnosis. Before deciding to undergo pre-symptomatic genetic testing for Huntington&rsquos disease, a person usually consults with a genetic counselor. The procedure is entirely optional, and the decision to undergo genetic testing can be emotionally difficult. Therefore, it&rsquos important to understand how genetic testing works, its risks and benefits, and consequences of test results. Informed consent is necessary more information on this process can be found here.

In general, genetic tests are performed on a sample of tissue or fluid. This can be a cheek swab, blood, urine, hair or amniotic fluid sample. Then, the sample is sent to a laboratory, where technicians analyze it and search for a change in protein level or in DNA.

For Huntington&rsquos disease, the genetic test is performed on a blood sample. Once it is sent to the laboratory, technicians perform a DNA test to look at the huntingtin gene, and specifically, to check for the expanded CAG repeat characteristic of HD. The goal of the test is to measure the number of repeats in the huntingtin gene. More information on DNA mutations and the CAG repeat expansion in Huntington&rsquos disease can be found here.

You can also watch a HOPES video on genetic testing here.

How does the genetic test for Huntington Disease work?

Laboratory technicians perform a set of steps to inspect the DNA provided in the blood sample. Let&rsquos take a closer look at each of these steps.

Step 1- The Polymerase Chain Reaction: Making many DNA copies for analysis.

The polymerase chain reaction, or PCR, is used to isolate DNA and make many copies of it. It is needed in order to make lots of copies of the huntingtin gene, allowing scientists to examine it more closely. PCR produces millions of DNA copies in a short amount of time, and includes a few steps as follows.

First, the DNA sample is heated to nearly 100 o C. DNA is normally double-stranded in a helix formation, but the heat causes the strands of DNA to separate into single strands. This process is called denaturation.

Then, the sample is cooled a little. Now, primers can bind to each DNA strand. These are small molecules serving as the starting material for a reaction called polymerization. The goal of this reaction is to create more DNA. An enzyme called DNA polymerase makes new DNA strands by adding nucleotides, the structural unit of DNA, to the primer on each strand. It&rsquos like adding building blocks to a pre-existing block tower. As more nucleotides are added, the strand is extended, and eventually, a new copy of the gene is made.

Step 2- Gel Electrophoresis: Separating fragments of DNA based on size.

After creating millions of copies of the huntingtin gene using PCR, we are now ready to separate DNA fragments, in order to inspect them more closely. This can be done using a technique called gel electrophoresis. The principle is simple: DNA fragments are separated based on their size because smaller fragments are able to travel through the gel faster than larger ones. Let&rsquos take a closer look at how exactly gel electrophoresis is done.

First, restriction enzymes attach themselves to DNA and cut it into small fragments. Then, the DNA pieces are placed in small wells in a gel floating horizontally in a buffer solution. This solution is located between two electrodes, one positive and the other negative. Once an electric current is passed through the gel, the fragments of DNA begin to move. DNA is negatively charged, so it is attracted to the positive electrode. The smaller fragments move faster than the larger ones, so they move across a greater distance towards the positive electrode.

Step 3- Inspection of DNA Fragments: How many CAG repeats?

Now that the fragments of DNA have been separated, the technicians are ready to inspect each DNA fragment. They do this to evaluate the number of CAG repeats in the huntingtin gene.

Individuals who do not have HD usually have 28 or fewer repeats. Individuals with HD usually have 40 or more repeats.

Information on test results and what they mean is available here.

For further reading

HOPES is a team of faculty and undergraduate students at Stanford University dedicated to making scientific information about Huntington’s disease (HD) more readily accessible to the public


Steering Committee and Working Groups

Steering Committee

The NIAID/DMID Systems Biology Consortium for Infectious Diseases Steering Committee was established by NIAID in collaboration with the awardees and for example, will attend the annual meetings, review the progress of the centers focused on antimicrobial resistance and infectious diseases. The Steering Committee members have broad scientific and clinical expertise and are not current collaborators of the funded programs.

Working Groups

The NIAID/DMID Systems Biology Consortium for Infectious Diseases established working groups composed of members from each of the centers to foster collaboration in areas of shared expertise and project milestones. The working groups focus on critical areas of the program: modeling, data dissemination, and clinical.


Approaches to integrative analysis of multiple omics data

Multi-omics approaches have been applied to a wide range of biological problems and we have grouped these into three categories, “genome first”, “phenotype first”, and “environment first”, depending on the initial focus of the investigation. Thus, the genome first approach seeks to determine the mechanisms by which GWAS loci contribute to disease. The phenotype first approach seeks to understand the pathways contributing to disease without centering the investigation on a particular locus. And the environment first approach examines the environment as a primary variable, asking how it perturbs pathways or interacts with genetic variation. We then discuss briefly some statistical issues around data integration across omics layers and network modeling.

The genome first approach

In the absence of somatic mutations, primary DNA sequence remains unaltered throughout life and is not influenced by environment or development. Thus, for disease-associated genetic variants, it is assumed that a specific variant contributes to, and is not a consequence of, disease. Such variants constitute a very powerful anchor point for mechanistic studies of disease etiology and modeling interactions of other omics layers. GWASs often identify loci harboring the causal variants, but lack sufficient power to distinguish them from nearby variants that are associated with disease only by virtue of their linkage to the causative variant. Moreover, the identified loci typically contain multiple genes, which from a genomic point of view could equally contribute to disease. Thus, although GWAS results may be immediately useful for risk prediction purposes, they do not directly implicate a particular gene or pathway, let alone suggest a therapeutic target. Locus-centered integration of additional omics layers can help to identify causal single nucleotide polymorphisms (SNPs) and genes at GWAS loci and then to examine how these perturb pathways leading to disease.

Analyses of causal variants at GWAS loci focused originally on coding regions, but it has become clear that for many common diseases regulatory variation explains most of the risk burden [21]. Thus, transcriptomics, employing either expression arrays or RNA-Seq (Box 1), has proven to be particularly useful for identifying causal genes at GWAS loci [79,16,, 22–24]. A number of statistical methods have been developed for examining causality based on eQTL at GWAS loci, including conditional analysis and mediation analysis (Fig. 2). Large datasets of eQTLs are now available for a number of tissues in humans and animal models [17, 22, 25, 26].

Usage of omics applications to prioritize GWAS variants. Locus zoom plot for a complex GWAS locus shows several candidate genes could be causal. Heatmap using various omics approaches for evidence supporting or refuting candidate causal genes. Beyond literature queries for candidates, various omics technologies and databases can be used to identify causal genes, including: searching for expression in relevant tissues [173,174,175], summary data-based Mendelian randomization (SMR) [176], mediation analysis [177], conditional analysis [23], correlation analyses, searching for overlapping pQTLs [178, 179], and/or implementing epigenetic data to narrow candidates (discussed for FTO locus [16])

Identification of causal DNA variants affecting gene expression is complicated as a variety of elements, within the gene and hundreds of kilobases away from the gene, can contribute. Results from the ENCODE (Encyclopedia of DNA elements) and RoadMap Consortia have been particularly useful in this regard for defining enhancer and promoters in a variety of tissues in mice and humans (Box 1, Fig. 3). Once the causal variants or gene have been established, other omics layers can help identify the downstream interactions or pathways. As discussed further below, transcript levels often exhibit poor correlation with protein levels and thus proteomics data are expected to be more proximal to disease mechanisms. Moreover, proteomics techniques such as yeast two-hybrid screens or “pulldown analyses” can be used to identify interacting pathways contributing to disease [27]. For certain disorders, metabolomics can also be used to bridge genotype to phenotype [28].

Genome first approach at FTO GWAS locus. Claussnitzer et al [16] combined genomics, epigenomics, transcriptomics, and phylogenetic analysis to identify the functional element, the causative SNP, and the downstream genes mediating the genetic effect at the FTO locus in obesity. Circles represent genes in the locus and yellow circles represent genes implicated by the respective omics data. a Genomics: the FTO locus, containing several genes (circles), harbors the most significant obesity-associated haplotype in humans. SNPs that are in linkage disequilibrium with the risk allele are color coded—blue represents the non-risk (normal) haplotype and red the risk haplotype. b Epigenomics: publically available epigenomic maps and functional assays were used to narrow down the original associated region to 10 kb containing an adipose-specific enhancer. Chromatin capturing (Hi-C) was used to identify genes interacting with this enhancer. c Transcriptomics: this technique was used to identify which of the candidate genes are differentially expressed between the risk and normal haplotypes, identifying IRX3 and IRX5 as the likely downstream targets. In addition, conservation analysis suggested that rs1421085 (SNP that disrupts an ARID5B binding motif) is the causative SNP at the FTO locus. CRISPR-Cas9 editing of rs1421085 from background (TT) to risk allele (CC) was sufficient to explain the observed differences in expression of IRX3 and IRX5. d Functional mechanism: correlation and enrichment analysis were then used to identify potentially altered pathways that were then confirmed by in vitro and in vivo studies

A good example of a genome first approach is the study by Claussnitzer and colleagues [16] that involved analysis of the FTO locus that harbors the strongest association with obesity (Fig. 3). To identify the cell type in which the causal variant acts, they examined chromatin state maps of the region across 127 cell types that were previously profiled by the Roadmap Epigenomics Project (Box 1). A long enhancer active in mesenchymal adipocyte progenitors was shown to differ in activity between risk and non-risk haplotype. They then surveyed long-range three-dimensional chromatin (Hi-C) interactions involving the enhancer and identified two genes, IRX3 and IRX5, the expression of which correlated with the risk haplotype across 20 risk-allele and 18 non-risk-allele carriers. To identify the affected biologic processes, Claussnitzer and colleagues examined correlations between the expression of IRX3 and IRX5 with other genes in adipose tissue from a cohort of ten individuals. Substantial enrichment for genes involved in mitochondrial functions and lipid metabolism was observed, which suggests possible roles in thermogenesis. Further work using trans-eQTL analysis of the FTO locus suggested an effect on genes involved in adipocyte browning. Adipocyte size and mitochondrial DNA content were then studied for 24 risk alleles and 34 non-risk alleles and shown to differ significantly, consistent with an adipocyte-autonomous effect on energy balance. Claussnitzer and colleagues confirmed the roles of IRX2 and IRX5 using experimental manipulation in primary adipocytes and in mice. Finally, the causal variant at the FTO locus was predicted using cross-species conservation and targeted editing with CRISPR-Cas9 identified a single nucleotide variant that disrupts ARID5B repressor binding.

The phenotype first approach

A different way to utilize omics data to augment our understanding of disease is to simply test for correlations between disease, or factors associated with disease, and omics-based data. Once different entities of omics data are found to correlate with a particular phenotype, they can be fitted into a logical framework that indicates the affected pathways and provide insight into the role of different factors in disease development.

For example, Gjoneska et al. [20] used transcriptomic and epigenomic data to show that genomic and environmental contributions to AD act through different cell types. The authors first identified groups of genes that reflect transient or sustained changes in gene expression and cell populations during AD development. Consistent with the pathophysiology of AD, the transcriptomic data showed a sustained increase in immune-related genes, while synaptic and learning functions showed a sustained decrease. The authors then used chromatin immunoprecipitation and next-generation sequencing (NGS) to profile seven different epigenetic modifications that mark distinct functional chromatin states. They were able to identify thousands of promoters and enhancers that showed significantly different chromatin states in AD versus control. Next, the authors showed that these epigenetic changes correspond to the observed changes in gene expression, and used enrichment analysis to identify five transcription factor motifs enriched in the activated promoters and enhancers and two in the repressed elements. Finally, the authors used available GWAS data to see whether genetic variants associated with AD overlap any of the functional regions they identified. Notably, they found that AD-associated genetic variants are significantly enriched in the immune function-related enhancers but not promoters or neuronal function-related enhancers. This led the authors to suggest that the genetic predisposition to AD acts mostly through dysregulation of immune functions, whereas epigenetic changes in the neuronal cells are mostly environmentally driven.

In another example, Lundby and colleagues [29] used quantitative tissue-specific interaction proteomics, combined with data from GWAS studies, to identify a network of genes involved in cardiac arrhythmias. The authors began by selecting five genes underlying Mendelian forms of long QT syndrome, and immunoprecipitated the corresponding proteins from lysates of mouse hearts. Using mass spectrometry (MS), they then identified 584 proteins that co-precipitated with the five target proteins, reflecting potential protein–protein interactions. Notably, many of these 584 proteins were previously shown to interact with ion channels, further validating the physiological relevance of this experiment. They then compared this list of proteins with the genes located in 35 GWAS loci for common forms of QT-interval variation, and identified 12 genes that overlapped between the two sets. This study provides a mechanistic link between specific genes in some of the GWAS loci to the genotype in question, which suggests a causative link in the locus.

The environment first approach

In this approach, multi-omics analyses are used to investigate the mechanistic links to disease using an environmental factor such as diet as the variable. To accurately assess environmental or control factors such as the diet in humans is very difficult and so animal models have proven particularly valuable for examining the impact of the environment on disease. Here, we give three examples of multi-omic study designs used to examine the impact of the environment on disease.

One kind of study design is to examine multiple environmental conditions to determine how these perturb physiologic, molecular, and clinical phenotypes. For example, Solon-Biet and colleagues [30] explored the contribution of 25 different diets to the overall health and longevity of over 800 mice. They compared the interaction between the ratio of macronutrients with a myriad of cardiometabolic traits (such as lifespan, serum profiles, hepatic mitochondrial activity, blood pressure, and glucose tolerance) in order to elucidate specific dietary compositions associated with improved health. The ratio of protein to carbohydrate in the diet was shown to have profound effects on health parameters later in life, offering mechanistic insight into how this is achieved.

The second study design seeks to understand the interactions between genetics and the environment. For example, Parks and coworkers [31, 32] recently studied the effects of a high fat, high sucrose diet across about 100 different inbred strains of mice. By examining global gene expression in multiple tissues and metabolites in plasma, they were able to identify pathways and genes contributing to diet-induced obesity and diabetes. In the case of dietary factors, the gut microbiome introduces an additional layer of complexity as it is highly responsive to dietary challenges and also contributes significantly to host physiology and disease. Recent multi-omic studies [31, 33, 34] have revealed an impact of gut microbiota on host responses to dietary challenge and on epigenetic programming.

The third type of study design involves statistical modeling of metabolite fluxes in response to specific substrates. For example, the integration of bibliographic, metabolomic, and genomic data have been used to reconstruct the dynamic range of metabolome flow of organisms, first performed in Escherichia coli [35] and since extended to yeast [36, 37] and to individual tissues in mice [38] and humans [39]. Other applications have explored various connections between metabolome models and other layers of information, including the transcriptome [40] and proteome [41,42,43]. Refinement of these techniques and subsequent application to larger population-wide datasets will likely lead to elucidation of novel key regulatory nodes in metabolite control.

Integration of data across multi-omics layers

A variety of approaches can be used to integrate data across multiple omics layers depending on the study design [44]. Two frequently used approaches involve simple correlation or co-mapping. Thus, if two omics elements share a common driver, or if one perturbs the other, they will exhibit correlation or association (Fig. 4). A number of specialized statistical approaches that often rely on conditioning have been developed. In these approaches a statistical model is used to assess whether each element of the model—for example, a SNP and expression change—contributes to the disease independently versus one being the function of the other. For example, a regression-based method termed “mediation analysis” was developed to integrate SNP and gene expression data, treating the gene expression as the mediator in the causal mechanism from SNPs to disease [45, 46]. Similar approaches have been applied to other omics layers [46, 47]. More broadly, multi-layer omics can be modeled as networks, based on a data-driven approach or with the support of prior knowledge of molecular networks. A practical consideration in multi-omic studies is the correlation of identities of the same objects across omics layers, known as ID conversion. This is performed using pathway databases such as KEGG and cross-reference tables [47]. Ideally, the multi-omics datasets will be collected on the same set of samples, but this is not always possible GWAS and expression data are frequently collected from different subjects. In such cases, it is possible to infer genetic signatures (eQTL) or phenotypes based on genotypes [48,49,50].

The flow of biologic information from liver DNA methylation to liver transcripts, proteins, metabolites, and clinical traits. A panel of 90 different inbred strains of mice were examined for DNA methylation levels in liver using bisulfite sequencing. CpGs with hypervariable methylation were then tested for association with clinical traits such as a obesity and diabetes, b liver metabolite levels, c liver protein levels, and d liver transcript levels. Each dot is a significant association at the corresponding Bonferroni thresholds across CpGs with the clinical traits and metabolite, protein, and transcript levels in liver. The genomic positions of hypervariable CpGs are plotted on the x-axis and the positions of genes encoding the proteins or transcripts are plotted on the y-axis. The positions of clinical traits and metabolites on the y-axis are arbitrary. The diagonal line of dots observed to be associated with methylation in the protein and transcript data represent local eQTL and pQTL. The vertical lines represent “hotspots” where many proteins or transcripts are associated with CpG methylation at a particular locus. Figure taken with permission from [180], Elsevier

Investigating the quantitative rules that govern the flow of information from one layer to another is also important when modeling multiple data types. For example, one of the fundamental assumptions behind many of the RNA co-expression networks is that fluctuations in RNA abundance are mirrored by proteins. However, while the tools for effective interrogation of transcriptome are widely available and commonly used, effective interrogation of proteomes at the population level is a relatively new possibility (Box 1). A number of studies have now shown that while levels of many proteins are strongly correlated with their transcript levels, with coincident eQTL and protein QTL (pQTL), the correlations for most protein–transcript pairs are modest [51,52,53,54,55,56,57,58]. The observed discordance of transcript and protein levels is likely to be explained by regulation of translation, post-translation modifications, and protein turnover. Together these studies suggest that RNA may be a good predictor of abundance of only some proteins, identifying groups of genes that confer to this rule and those that do not. In the context of disease oriented research, such studies constitute an important step for creating an analytical framework that will later be applied to interpretation of disease-specific datasets. In addition, especially in context of limited availability of human samples, such studies are useful for choosing among possible experimental approaches.

A key concept of modern biology is that genes and their products participate in complex, interconnected networks, rather than linear pathways [59]. One way to model such networks is as graphs consisting of elements that exhibit specific interactions with other elements [60,61,62,63,64]. Such networks were first constructed based on metabolic pathways, with the metabolites corresponding to the nodes and the enzymatic conversions to the edges [65, 66]. Subsequently, networks were modeled based on co-expression across a series of perturbations with the genes encoding the transcripts corresponding to the nodes and the correlations to the edges [67,68,69]. In the case of proteins, edges can be based on physical interactions, such as those identified from global yeast two-hybrid analyses or a series of “pulldowns” [27]. Networks can also be formed based on genomic interactions captured by HiC data [70, 71], and physical interactions can also be measured across different layers, such as in ChIP-Seq, which quantifies DNA binding by specific proteins.

For studies of disease, co-expression networks can be constructed based on variations in gene expression that occur among control and affected individuals separately [72,73,74]. Comparison of network architecture between control and disease groups allows the identification of closely connected nodes (“modules”) most correlated with disease status. In general, co-expression or interaction networks are “undirected” in the sense that the causal nature of the interactions is unknown. Interaction networks can be experimentally tested, although the high number of suggestive interactions identified in each study makes indiscriminate testing prohibitive. If genetic data, such as GWAS loci for disease or eQTLs for genes, are available it may be possible to infer causality using DNA as an anchor [75,76,77]. Such integration of genetic information with network modeling has been used to highlight pathways that contribute to disease and to identify “key drivers” in biologic processes [72,73,74, 78]. For example, Marbach and colleagues [79] combined genomics, epigenomics, and transcriptomics to elucidate tissue-specific regulatory circuits in 394 human cell types. They then overlaid the GWAS results of diseases onto tissue-specific regulatory networks in the disease-relevant tissues and identified modules particularly enriched for genetic variants in each disease. In another example, Zhang and coworkers [64] examined transcript levels from brains of individuals with late onset AD and analyzed co-expression and Bayesian causal modeling to identify modules associated with disease and key driver genes important in disease regulatory pathways. Together, these studies illustrate how network analysis can be used to narrow down the focus of disease research into specific functional aspects of particular cell types or tissues, considerably facilitating downstream mechanistic efforts and hypothesis generation.


The Bioinformatics and Computational Biosciences Branch (BCBB) offers a suite of scientific services and resources for the NIAID research community and its collaborators. BCBB provides expertise and computational solutions to researchers at all levels of experience.

The NIAID-funded Bioinformatics Resource Centers provide data-driven, production-level, sustainable computational platforms to enable sharing and access to data, portable computational tools, and standards that support interoperability for the infectious diseases research community.


Watch the video: ΤΟ ΜΕΛΛΟΝ ΤΩΡΑ (June 2022).


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