Infection outcome needs two to tango: human host and the pathogen

Abstract

Infectious diseases are potential drivers for human evolution, through a complex, continuous and dynamic interaction between the host and the pathogen/s. It is this dynamic interaction that contributes toward the clinical outcome of a pathogenic disease. These are modulated by contributions from the human genetic variants, transcriptional response (including noncoding RNA) and the pathogen’s genome architecture. Modern genomic tools and techniques have been crucial for the detection and genomic characterization of pathogens with respect to the emerging infectious diseases. Aided by next-generation sequencing (NGS), risk stratification of host population/s allows for the identification of susceptible subgroups and better disease management. Nevertheless, many challenges to a general understanding of host–pathogen interactions remain. In this review, we elucidate how a better understanding of the human host-pathogen interplay can substantially enhance, and in turn benefit from, current and future applications of multi-omics based approaches in infectious and rare diseases. This includes the RNA-level response, which modulates the disease severity and outcome. The need to understand the role of human genetic variants in disease severity and clinical outcome has been further highlighted during the Coronavirus disease 2019 (COVID-19) pandemic. This would enhance and contribute toward our future pandemic preparedness.

Introduction

Even today, one of the leading causes of morbidity and mortality globally is infectious diseases [1–5]. This highlights the functional role of widespread infectivity of the pathogen/s that is leading to mortality among human population/s. The rapid emergence of infectious diseases within the last two decades and their exacerbation to a pandemic scale with possible role of globalization is increasing the risk of life-threatening infections, both acute and chronic. This also has challenged the healthcare infrastructure globally with economic consequences. The outcome of the disease is determined by the dynamic interplay between the host and the pathogen. We summarize here the modern tools, techniques and scientific discoveries, which have been important to understand this relationship and the associations thus observed. The need for multidimensional studies combining genomic data of both host and pathogen, overlaid with clinical and demographic status of patients, would be pivotal in the modern era. In the current ongoing pandemic, initiatives such as COVID-19 Host Genetics Initiative serve as an important advancement to understand and highlight the milieu of host–pathogen interactome. The initiative paves the way for comprehensive meta-analysis projects bringing worldwide researchers together to identify determinants of COVID-19 susceptibility, severity and outcomes. Simultaneously, it has laid the groundwork for diagnostic markers and therapeutic target discovery [6].

Human genetic diversity

The genetic basis of diseases is a reflection of the evolution of the human genome. One of the fundamental characteristics that portray the host component in pathogenic diseases is the fact that life-threatening clinical disease is manifested only in a small percentage of infected individuals. This variation highlights the importance of studying human genetic diversity in the context of pathogenic diseases. To understand these relevant genetic components, an understanding of differences in the human genome is important. Approximately 90% of human allelic variations are polymorphisms that date back to our African origin [7]. New mutations arise in the human population naturally at the rate of 175 mutations per diploid human genome per generation [8].

Importance of studying genetic diversity in the background of pathogenic diseases

During the current COVID-19 global pandemic, identifying a susceptible group of population can significantly modulate the outcome of the pandemic vis-a-vis human population. This includes priority healthcare access and close monitoring. Thus, any leads in that direction are too important to be undermined. The history of human genetic susceptibility in pathogenic disease outbreaks dates back to the identification of resistance factors for the disease, inclusive of the individual’s heterozygous allele state for sickle variants of erythrocytes having resistance to malaria in the 1950s [9, 10]. Another factor is the lack of expression of the Duffy antigen receptor for chemokines (DARC) on red cells due to single nucleotide polymorphism (SNP) rs2814778 leading to a negligible infection in Western and Central Africa [10, 11].

Similarly, the Delta 32 mutation at rs333 in the entry receptor C-C chemokine receptor type 5 (CCR5) for Human Immunodeficiency Virus-Acquired Immunodeficiency Syndrome (HIV-AIDS) [12] confers resistance to the individual [13]. This mutation is understood to have evolved 700 years ago under the pathogenic evolutionary pressure of bubonic plague [14]. The survival of these alleles from the pandemic in the current human populations underscores the need to identify associations of host genetic elements with respect to infectious diseases. Extending the implications of the above example, studies can be used to classify a population according to the risk of acquiring infection and severe clinical manifestations of the disease. A well-known example is the O blood group in ABO blood grouping. The O blood group confers a protective effect to host in COVID-19 [15], increasing the susceptibility to cholera [16–18]. Furthermore, along with the O blood group, Rh-negative type compared to other blood groups was less susceptible to SARS-CoV-2 infection and had better clinical outcome. In addition, the AB group is known to require increased respiratory support (invasive), and is at higher risk of death due to COVID-19 [19]. Genome-wide association study (GWAS) by Genetics Of Mortality In Critical Care (GenOMICC) has studied the critically ill COVID-19 patients in UK to discover host genetic variants associated with critical illness. The study has highlighted that although ABO locus was previously associated with COVID-19, it did not show the same in their study. But the presence of signal close to genome-wide significance at the ABO locus potentially indicates its role in COVID-19. Whether ABO locus is associated with critical illness or not - is a matter of future research and more studies in this direction would be essential for in-depth understanding.

This is possibly relevant for large number of pathogenic diseases that have no/asymptomatic effect on a subset of the population. An example of such a disease previously shown to have a major impact on global food supply is the Creutzfeldt–Jakob disease caused by the prion protein. An SNP (rs1799990) leading to heterozygosity of methionine/valine at 129-codon of their prion protein gene confers immunity to the disease [20]. In the modern age endeavor toward personalized medicine and continual decrease in the cost of next-generation sequencing (NGS) enabled human genome sequencing, it is possible to understand and elucidate the genomic architecture of any given population. This information when overlaid with the rate at which diseases are spreading through a population can give us insights into population susceptibility for infection/s. The targeted administration of therapeutic interventions on susceptible groups of populations could reduce the load on medical infrastructure.

Diseases and its associated human host genetic variation

Statistical and genomics based measures are used to understand the host component involved in disease susceptibility. Human genetic components driving disease infection and prognosis were identified by a great variety of approaches. This includes twin studies, linkage analysis, complex segregation analysis and whole-genome sequencing approaches such as GWAS.

Assessment of human genetic components in twin studies identified 86% of hereditary components in Measles [21]. Even tuberculosis has a genetic component as highlighted by twin studies [22] which were later proven to be the result of defects in IL12/IFNγ dependent signaling pathway leading to a condition termed Mendelian susceptibility to mycobacterial disease (MSMD). In addition, Hepatitis B has also shown different associations in twin and population studies [23–25]. Furthermore, Hepatitis B viral clearance associated with hepatocellular carcinoma has host genetic elements modulating disease severity, such as human leukocyte antigen HLA-DP and HLA-DQ loci [26].

Moreover, an unbiased view of genomes of affected individuals is provided by GWAS, which has revolutionized the area of disease genetics allowing the field to move out of the candidate gene approach [27]. It has enabled the identification of many lead SNPs associated with diseases. The GWAS catalog is maintained by EMBL at https://www.ebi.ac.uk/gwas [28]. The GWAS Catalog contains 5037 publications and 257 351 variant-trait association (as on 5 May 2021). GWAS studies require large sample sizes as millions of genomes and variants are analyzed together. It is ideal to have a population set with a homogeneous ethnic background to avoid spurious associations [29]. Mycobacterium leprae or M. lepromatosis causes leprosy by long-term infection damaging the nervous system, eyes and respiratory tracts. Using GWAS, several candidate genes have been observed as host genetic factors modulating disease severity, including TNFA, IL10, PARK2 in the past and more recently LACC1 [30]. Genetic components and mutations in the interleukin-coding genes have been observed to be involved in many diseases as shown in Table 1.

Table 1.

List of infections with associated human genetic variation

Disease	Associated genetic element	Study methodology	Disease	Reference
COVID-19	SLC6A20, LZTFL1, CCR9, FYCO1, CXCR6, XCR1, ABO, OAS, TYK2, DPP9, IFNAR2, CCR2	GWAS	COVID-19	[15, 31, 32]
AIDS	CCR5	GWAS, PCR, PCR-RFLP	AIDS	[33, 34]
Hepatitis	IL28B	GWAS	Hepatitis	[35–37]
Hepatitis	Different HLA types, TLR-3, TLR-9, NTCP	GWAS	Hepatitis	[25, 38–43]
Dengue	MICB, TNF, CD209, FcγRIIA, TPSAB1, CLEC5A, IL10, PLCE1	GWAS	Dengue	[44–46]
Malaria	TLRs, TNFs, HBB, ABO, ATP2B4	PCR-RFLP and sequencing, PCR, snp directed seq, GWAS	Malaria	[47–52]
Tuberculosis	TLRs, IFN-γ, AGMO, FOXP1, UBLCP1	GWAS, Candidate gene approach	Tuberculosis	[53–58]
Leprosy	IL10, PACRG, NOD2, HLA-DRB1/DQA1, LTA, GATA3, IFNG, TLR1	Case control, GWAS	Leprosy	[30, 59–64]
Meningococcal disease	CEACAM, SPLUNC1, CFH/CFHR3, IL-1, Compliment factors	Candidate gene approach, GWAS	Meningococcal disease	[65–69]
Creutzfeldtjakob disease	PRNP	GWAS	Creutzfeldt Jakob disease	[70]
Pneumonia	MBL2, CD14, IRAK-4, MyD88, TIRAP	Candidate gene approach, GWAS	Pneumonia	[71–76]
MSMD	IFNGR1, IFNGR2, STAT1, IL12B, IL12RB1, ISG15, IRF8, NEMO, CYBB	Candidate gene approach, GWAS	MSMD	[77–80]
Cold Sores	TLR3, TRAF3, UNC93B1, KIF1B	Candidate gene approach, GWAS	Cold Sores	[81–84]
Warts, Cervical cancer	CXCL12, KLF12, NR5A2, MIR365, ARRDC3	Case control, GWAS	Warts, Cervical cancer	[85–87]
Gastroenteritis	FUT2, FUT3, ABH	Candidate gene approach, GWAS	Gastroenteritis	[88–90]
Candidiasis	TLR1, TLR3, Dectin-1, CARD9, STAT1	GWAS, Candidate gene approach, mice model	Candidiasis	[91–94]
Skin/respiratory Infections	DAPK3, XRN1, IL4, DEFB1, CRP, VDR	GWAS, Mice Models	Skin/respiratory Infections	[95–100]
Whipple’s disease	HLA-B27, IRF4	GWAS	Whipple’s disease	[101–103]
Infectious mononucleosis, cancers	MDC1, RAD54L, TP53BP1, RPA1, LIG3	Candidate gene approach, Genotyping	Infectious mononucleosis, cancers	[104, 105]
Influenza	IFITM3, IRF7, TMPRSS2, TLR3	PCR amplification and sequencing, GWAS	Influenza	[106–110]
Mononucleosis, pneumonia	TLRs, MBL	PCR-RFLP and sequencing,	Mononucleosis, pneumonia	[111–115]
Respiratory infections	IFN-γ, IL-4, SLC39A1	GWAS, Case control studies	Respiratory infections	[116–119]

Host components and COVID-19

The current COVID­19 pandemic is characterized by complexity of clinical phenotypes, with the majority of severe acute respiratory syndrome coronavirus 2 (SARS-CoV­2) infections being asymptomatic or mild (Figure 1). In brief, we are making an effort toward threading the available literature on host factors and clinical characteristics leading to variability in disease outcome of COVID-19.

Figure 1 .

COVID-19 and host association. The wealth of literature and the discoveries highlight the multicomponent role of the differential factors and their role in the current COVID-19 pandemic. The subcomponents of the major factors have been captured in the figure.

Ethnic diversity, one of the fundamental host population characteristics, has a role toward susceptibility in pathogenic diseases, even in the case of COVID-19. Blacks, South Asian populations such as Pakistanis have an elevated risk of contracting COVID-19 and are highly probable toward SARS-CoV-2 infection, as highlighted in the UK-based population study. It is important to note that Niedzwiedz et al. [120] also observed increased association of higher risk of infection (without hospitalization) in those who are socioeconomically disadvantaged and with no qualifications. At the same time, adjustment for the above factors only led to modest attenuation for the hospital cases. The meta-study from 49 562 COVID-19 patients from 46 studies across 19 countries worldwide associated and categorized the mutations according to their prevalence in COVID-19 diagnosed, hospitalized and critical cases. The study also reported 13 SNPs with significant associations to the genes: SLC6A20, LZTFL1, RPL24, FOXP4, TMEM65, ABO, OAS1, KANSL1, TAC4, DPP9, RAVER1, PLEKHA4 and IFNAR2 [121]. Moreover, the 3p21.31 gene cluster is associated in COVID-19 and it increases susceptibility to severe disease manifestation [15].

Other determinants such as age, gender and comorbidities are also shown to modulate the clinical variability of COVID-19. These factors are looked primarily from the standpoint of human SARS-CoV-2 receptors angiotensin-converting enzyme 2 (ACE2), entry point for SARS-CoV-2 and a directly associated serine protease involved in SARS spike protein cleavage-Transmembrane protease, serine (TMPRSS). In older patients, higher severity and mortality of the disease was reported and explained by age-related dynamics of host factor expression of ACE2 [122]. A 10-year increase in age showed a 1.2-fold increase in ACE2 expression. A lower nasal epithelial ACE2 expression and COVID-19 prevalence is reported in children [123, 124], although no decrease in ACE2 protein levels is reported in children. Moreover, this observed correlation needs further investigation, as some studies have found no correlation between ACE2 expression and COVID-19 pulmonary risk factors among children [125].

In contrast to the above cited examples, large population studies with severe COVID-19 evaluated independent risk factors such as male gender, asthma, cardiovascular disease, chronic obstructive pulmonary disorder (COPD), diabetes and smoking, compared to age-matched healthy controls. The findings indicate no significant difference in ACE2 localization [125–127]. However, the role of ACE2 variation in susceptibility to SARS infection needs further studies and investigations as similar associations with entry receptors have been reported during other viral diseases such as MERS [128] and HIV [129]. Additionally, SNPs in the entry receptor (ACE2) and TMPRSS2 may also affect the contrasting reports.

Studies showed that there is no correlation between ACE2 polymorphisms and COVID-19 susceptibility [130, 131]. Although, the increased frequencies of ACE2 SNPs (rs4830542, rs4240157, rs2074192, rs233575 and rs879922) in the European and the admixed American population have been associated with severe illness, while lower frequencies of the same confers protection in East and South Asian populations [132]. The SNP rs2285666 has been associated with different disease outcomes in the same population [133]. The G allele is associated with increased infection and fatality risk, whereas the A allele has been reported in milder cases of infection [134].

Few SNPs, rs73635825 (S19P) and rs143936283 (E329G), were observed in an in silico study [135]. Although numerous studies have reported the association of SNP’s in the ACE2 gene and COVID outcome, the functional role of these SNPs has been studied by Hashizume et al. [136]. The study reported that seven globally identified SNPs had no effect on gene expression levels of ACE2 or virus infectivity. The SARS-CoV-2 spike protein is activated by cathepsin-mediated or II transmembrane serine proteases (TTSPs) mediated cleavage, thus leading to spike protein binding and viral entry into the host cell. A study has identified two Expression quantitative trait loci (eQTL) (rs12329760 and rs75603675) that may confer COVID-19 susceptibility differences using the QTLbase database [137].

Seeing the non-reproducibility of SNP studies among different ethnic groups, screening of ACE2 and TMPRSS2 SNPs in specific populations could be something important to explore in the future. These variabilities indicate and necessitate further studies to identify the involvement of other host factors modulating COVID-19 susceptibility and severity.

Gene expression differences driven by noncoding genetic diversity lead to inter-individual variability in pathogenic diseases

Other than genetic diversity in the coding region, a major factor contributing to phenotypic diversity among the human populations is the difference in gene expression levels [138]. Further, the variation in gene expression levels is also attributed to the genetic diversity in the noncoding region of the genome. In this section, we discuss in detail the functional role played by the genetic variations in the noncoding region of the genome which has functional role in modulating the downstream gene expression. This is corroborated by the fact that most of the GWAS studies report that mutations are present in noncoding regions [139]. A 17% difference in gene expression was observed between African and European populations [140], which are also subsequently validated in larger studies as well [141]. The highly conserved genomic regions inclusive of enhancer, promoters and transcription factor binding sites, among others, are genetic factors that modulate gene expression. Transcriptome-wide association studies (TWAS) functionally annotate the effect of SNPs at the transcriptional level and identify regulatory SNPs called eQTLs [142, 143]. For COVID-19, the study [32] identified SNPs rs10735079, rs74956615, rs2109069 and rs2236757 by GWAS. Then, using TWAS the same group associated disease severity with increased expression of oligoadenylatesynthetase 3 (OAS3) and with C-C chemokine receptor type 2 (CCR2) around rs10735079 and rs1138594, respectively, in lung tissue [15]. Similarly, in latent tuberculosis, SNP rs62292160 was shown to increase the expression of IL4 [144]. In Sporadic Creutzfeldt–Jakob disease (CJD), increased expression of STX6 in multiple brain regions was associated with the risk of disease contraction. The SNPs rs12754041, rs10797664 and rs6425657, each in strong linkage disequilibrium with the SNP rs3747957, showed a high probability of being causal [145]. The advancement in genomic tools has made it possible to identify and find associated elements with gene expression levels. This paves the way to discover the functional mechanisms causing the underlying alterations and thereby, aid in targeting and developing therapeutics.

Beyond gene expression: RNA maturation and transposable elements in infectious disease outcome

Various studies have reported the differential expression of cytokine, chemokine and interferon genes and their possible association with disease severity, in case of sepsis, and MTB infection [146–148]. This leads us to think whether the differential gene expression is the sole determinant of disease severity? Of the plausible other factors with functional role in disease severity, alternate transcripts and noncoding RNA seems to be important modulators.

One of the crucial steps in RNA maturation is alternative splicing (AS) that allows the retention of exons, or parts of exons, and introns in mature transcripts and causes the proteome diversity expansion. Infections can cause global changes in the alternate splicing pattern, which could be due to intrinsic factors such as polymorphism at the splice sites, signaling events or due to direct intervention by virulence factors. Recent studies have shown a change in AS landscape in host cells during a viral infection [149]. In vitro studies of HIV infection identified alternative conformations for the HIV-1 Rev-responsive element (RRE) and 5′ untranslated region (UTR), increasing the possibility of alternative structures playing an important part in the transport of viral RNA from nuclease and in its subsequent packaging in virions [150]. For better understanding of the fundamental question, if the RNA structure affects splicing, it is possibly pertinent to distinguish multiple conformations for the same sequence in cells. The expression of genes in HIV-1 from the same primary transcript is aided by the ability of RNA to form alternative conformations at critical splice sites [151].

Transposable elements, particularly L1 and Alu elements, are capable of introducing novel splice sites [152]. Indeed, Alu insertions into a gene introduces both splice acceptor and donor sites, and thereby holds potential of creating new exons [153]. Most Alu-derived exons undergo AS, contributing to transcript diversity. Moreover, mRNA translation is regulated by the enriched presence of Alu in the 5′UTR of human genes. Additionally, numerous AS events of Alu-derived exons are tissue-specific, possibly suggesting TE contribution to cell type, defined by transcriptome differences [154–156]. Human genes make use of alternative polyadenylation (polyA) sites, and TEs render the cues for some of these events, suggesting the role of TEs in regulating the 3′ end processing of host transcripts. Transcript diversity is further promoted by TEs, by providing alternative promoters for host genes. High-throughput techniques have manifested the all-round role of TEs as alternative gene promoters, and their contribution to tissue-specific expression profiles in normal tissues.

Several mechanisms, including genetic and epigenetic pathways, are known for intronic Alu-mediated gene expression. An intronic Alu polymorphism within the ACE gene was shown to be associated with the SARS-CoV-2 infection severity and morbidity [157, 158]. Many studies have highlighted Alus as a key modulator of gene expression with involvement in diverse physiological processes [159, 160]. Knowing this, along with the knowledge of human demographics, the role Alu polymorphisms in the host response to SARS-CoV-2 infection becomes worthy to consider, especially the Alu polymorphism in the key genes for immune response. The retrotransposition of Line1into chromosomal DNA may result in genomic instability, whereas reverse transcription in the cytosol may activate innate immune sensors [161]. Jones et al. [161] proved that HIV-1 infection enhances L1 retrotransposition in Jurkat cells in a Vif- and Vpr-dependent manner. They also reported extrachromosomal L1 DNA buildup in primary CD4+ T cells as an outcome of HIV-1 infection. These data indicate an unexplored interaction between HIV-1 and endogenous retrotransposable elements, with possible role in the regulation of innate immune response to HIV-1 infection, genomic instability and cytopathicity associated with the infection. Alternate transcripts and transposable elements have been extensively studied with respect to metabolic disorders and in response to stress conditions. Studies focused to elucidate the role of these elements and of noncoding RNA in the context of infectious diseases may be an important area of research to explain and understand the observed diversity of clinical outcome.

Disease severity from the aspect of pathogen

In the previous sections we have discussed the host components modulating the disease outcome. However, the outcome of an infection is determined by the complex interplay of the pathogen and the host immune system. Pathogen virulence and their role in the mortality of the host are also defined by the nature of the pathogen. Hence, the need to understand and factor in the pathogen’s role in disease outcome is highlighted in this section.

M. tuberculosis, the leading cause of infectious disease mortality, is classified into eight lineages (Lineage 1–8) having diverse geographical host associations. The L1–3, L4, and L5-6, and L7 are restricted to Asia, Europe-American, West Africa and Ethiopia-specific populations, respectively [162]. Recent studies have shown the sub-lineages of the L2 and L4 to be associated with higher virulence and are more geographically widespread [163]. This increased virulence is attributed to the delay or decreased host immune response causing enhanced transmission of the infection [164, 165]. Additionally, the severity of the L2 lineage infection when associated with host CD209_336 A/G SNP in patients leads to poorer outcomes and are at increased risk of mortality [166]. Apart from pathogens, virulence factors (VFs) are the critical elements essential in determining both the basis of cellular and molecular pathogenesis. VFs aid in the establishment of colonies [167–169], modulating host mechanisms for replication and survival [170–173] and evasion of immune response [174, 175].

Interestingly, while known to cause asymptomatic infections, H. pylori virulence factors cytotoxin-associated gene A (CagA), vacuolating cytotoxin A (vacA) and blood group antigen binding adhesin (BabA) are associated with the development and severity of diseases including peptic ulcer disease, gastric adenocarcinoma and gastric high-grade B cell lymphoma [176–179]. The chromosomal integrity of the cag-pathogencity (cag-PAI) island or the lack thereof has been associated with pathological progression. Severe pathology is linked with the clinical isolates of H. pylori having deletion or rearrangement in the cagA promoter. Intact cag-PAI was reported to be in the strains from East Asian ancestry than in the European and African strains [180]. This variation may be indicative of the certain subset of populations being susceptible to severe disease outcome, while the remaining population could be predisposed to benign disease condition only, even if infection occurs.

Finally, clinical isolates and/or variants also characterize the infection and the development of disease. Possibly, the most relevant example of the role of variants in the diverse clinical presentation and disease development is that of SARS-CoV-2 and its numerous variant of concern (VOC) induced infection [181, 182]. SARS-CoV-2 virus being a positive sense RNA genome is more prone to genomic modifications, including deletion mutations and SNPs leading to the selection of the virus either toward increased or decreased virulence. The D614G mutation in the surface glycoprotein region along with P323L in the RNA-dependent RNA polymerase (RdRp) is among the few mutations that have become globally predominant. The predominance could be explained by the higher viral load in the host [183] causing increased infectivity by D614G. Mutations have also been associated with the clinical outcome of the disease. A study by Nagy et al. [184] have shown association of five mutations, L84S in the ORF8 protein, L37F in the NSP6 protein, G196V in the ORF3a protein, F308Y in the NSP4 protein and the S197L mutation, in the nucleocapsid phosphoprotein with mild cases. Additionally, they also reported 15 mutations within seven genes: L54F, D614G and V1176F in the surface (S) glycoprotein, A97V and P323L in the RdRp, Q57H and G251V in the ORF3a protein, P13L, S194L, R203K, G204R and I292T in the nucleocapsid phosphoprotein, I33T in the ORF6 protein, S1197R and T1198K mutations in the NSP3 protein are associated with the severe cases of COVID-19.

Continuous evolution of SARS-CoV-2, new mutation and their selection and subsequent emergence of lineages may confer it an evolutionary advantage. Constant and continuous genomic surveillance of mutations will not only be extremely useful in keeping track of viral evolution but also would be resourceful in the development of the vaccines.

Future perspectives

NGS technologies today have accelerated the identifications and characterization of pathogenic organisms, especially detection of emerging variants [185]. The expansion of current techniques to identify and stratify host populations on the basis of infectious disease risk will improve our understanding of host–pathogen interaction and the role of host factors in modulating disease severity.

More studies are required to thread together the information across hierarchies and integrate genomic studies of both the pathogen and host along with respective epidemiological, transcriptomic, and clinical information. The threading of all facets including human genetic diversity, genetic elements, genes, noncoding RNA, transposable elements of the host as well as the pathogen, in the bottom-top approach, may link and piece together valuable information (Figure 2). The information thus gathered would aid in the identification of targets for drugs and therapeutics. Furthermore, the formulation of specific hypotheses based on population-wide studies would provide us with anticipated knowledge for experimental testing.

Figure 2.

Key players in delineating clinical outcome of pathogenic diseases. The outcome of the epidemic/pandemic at the local and global level has been modulated by many factors, inclusive of human genetic diversity, transcriptome, noncoding RNA and the pathogen/s genomic architecture. The multicomponent aspect highlights the role of integrative genomics for better understanding of disease severity and mortality.

Key Points

• Human genetic variants are an important modulator of the host response to pathogen infection.

• Among other factors, the host response at RNA level is shaped by differential expression of genes, alternate transcripts and the noncoding RNA.

• The pathogen genome architecture and its ability to elicit or evade immune response is also an important factor.

• Integrative Genomics of host-pathogen is integral to understand and elucidate the INTERACTOME shaping disease severity and outcome.

• Identification of population subgroups toward susceptibility/protection against infection would help public health decision making.

Ranjeet Maurya: My research aims to elucidate and understand the role of host response in modulating pathogenicity through application of integrative genomics approaches.

Akshay Kanakan: I am interested in expanding the applications of genomics toward development of Point-of-Care diagnostics.

Janani Srinivasa Vasudevan: I am interested to understand the host–pathogen interaction from the Genomics perspective.

Partha Chattopadhyay: My research is toward elucidating the role of noncoding RNA in modulating responses to pathogen challenge.

Rajesh Pandey: My lab is working toward INtegrative GENomics of HOst-PathogEn (INGEN-HOPE).

Funding support

This research was funded by the Council of Scientific & Industrial Research (CSIR) [Project code: MLP-2005]; Fondation Botnar [Project code: CLP-0031]; Indo-US Science and Technology Forum (IUSSTF) [Project code: CLP-0033]; and Intel Corporation [Project code: CLP-0034]; Bill and Melinda Gates Foundation [Project code: CLP-0036].

Conflict of interest

Authors wish to declare to have no conflict of interest.