TECHNICAL FIELD

The present invention relates to the field of genetic engineering. In particular, this invention relates to a genetically engineered cell and its uses thereof.

BACKGROUND

Infectious disease remains a major cause of morbidity and mortality worldwide. Tuberculosis and shigellosis, caused by intracellular bacterial pathogens, account for millions of deaths in recent time. Global travel, population growth, and antibiotic resistance increase the risk for epidemics of infectious diseases. While such public health crisis continues to worsen, insufficient antimicrobial drugs are being developed. In addition, long-term antimicrobial treatment can cause adverse effects to the patients, such as inflammatory tissue damage and microbiome dysbiosis. Therefore, alternative therapeutic approaches are urgently needed.

SUMMARY

In one aspect, there is provided an immune cell comprising a Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) system comprising a genome-targeted nuclease and/or a guide RNA (gRNA) that hybridizes to a sequence of a DNA molecule in the cell.

In some embodiments, the genome-targeted nuclease is any nuclease selected from the group consisting of Cas9, dCas9-Krab, and Cpf1 (Cas12a), or variants thereof.

In some embodiments, the gRNA is provided as a single guide RNA (sgRNA).

In some embodiments, the CRISPR system excises and/or inactivates one or more gene and/or gene products related to a system selected from the group consisting of immune response, cell metabolism, cell survival, cell growth, and cytotoxicity.

In some embodiments, the CRISPR system is a CRISPR-Cas9/dCas9-KRAB system.

In some embodiments, the immune cell is an innate immune cell.

In some embodiments, the immune cell is a phagocytic immune cell selected from the group consisting of a monocyte, a macrophage, a dendritic cell, and an antigen presenting cell.

In some embodiments, the immune cell is a cultured cell line, optionally a monocytic cell line, optionally a human monocyte cell line, optionally a THP-1 cell and/or RAW 264.7. In another aspect, there is provided a method for identifying an agent that enhances an immune response to a pathogen, comprising providing an immune cell or a host cell comprising a CRISPR system comprising a genome-targeted nuclease and/or a gRNA that hybridizes to a sequence of a DNA molecule in the cell, contacting the immune cell or the host cell to the pathogen in absence or presence of the agent, and determining the responses elicited in the immune cell or the host cell to the pathogen in the presence of the agent relative to the responses elicited in the immune cell or host cell to the pathogen in the absence of the agent.

In some embodiments, the pathogen is one selected from the group consisting of Mycobacterium tuberculosis (MTB), Mycobacterium bovis (strain bacillus Calmette- Guerin (BCG)), Salmonella enterica, Salmonella bongori, Listeria monocytogenes, Legionella spp (such as Legionella pneumophila), and Shigella flexneri.

In some embodiments, the pathogen is a drug-resistant bacterium.

In some embodiments, the genome-targeted nuclease is any nuclease selected from the group consisting of Cas9, dCas9-Krab, and Cpf1 (Cas12a).

In some embodiments, the gRNA is provided as a single guide RNA (sgRNA).

In some embodiments, the immune cell is an innate immune cell, optionally a phagocytic immune cell, optionally the immune cell is selected from the group consisting of a monocyte, a macrophage, a dendritic cell, and an antigen presenting cell.

In some embodiments, the host cell is an epithelial cell.

In some embodiments, the immune cell/ host cell is a cell from a subject having a disease or disorder selected from the group consisting of diseases of innate immunity, neurodegenerative disease and autoimmune disease.

In yet another aspect, there is provided a method of treating a bacterial infection in a subject in need thereof, comprising administering one or more modulator of one or more targets referred to in one or more tables selected from the group consisting of Table 1 , Table 2, Table 4, Table 5, Table 7A, Table 7B, Table 8A, Table 8B, and Table 9 into the subject.

In some embodiments, the infection is a mycobacterial infection, the modulator activates or inhibits one or more targets selected from the group consisting of i. Type I interferon pathway (such as, but is not limited to, TYK2, IFNAR1 , IFNAR2, STAT2, JAK1 , and IRF9); ii. Lipid metabolism pathway (such as, but is not limited to, AHR, ARNT, HELZ2, and PITPNB); iii. PI3K/AKT pathway (such as, but is not limited to, USP7, IRAKI , and CHUK); iv. Innate immunity pathway (such as, but is not limited to, YPEL5, MEM01 , and UBA7); v. Apoptosis pathway (such as, but is not limited to, WDR26, BAX, and BAK1 ); vi. TCA cycle pathway (such as, but is not limited to, NNT); vii. Other signalling pathways (PHIP and TRERF1); optionally the modulator is an inhibitor of TYK2, JAK1 and/or AHR.

In some embodiments, the infection is a Shigella infection, the modulator activates or inhibits one or more targets selected from the group consisting of i. Chromatin modifying pathways (such as, but is not limited to, MSL2, KMT2D, KMT2C, and KDM4A); ii. Toll-like receptor signalling (such as, but is not limited to, TRAF6, TIFA, IRAKI and MYD88); iii. Pyruvate metabolism (such as, but is not limited to, PDHB, DLAT, PDHA1 , CS, MPC1 and MPC2); iv. genes involved in antimicrobial peptides production pathways (such as, but is not limited to, HDAC2, and CREBBP); v. PIP3/AKT signalling pathways (such as, but is not limited to, LAMTOR3, MAP2K7, MAPK9 and LAMTOR2), optionally the modulator activates or inhibits one or more target selected from the group consisting of IRAK1/4, PDHB, MSL2, and TRAF6.

In some embodiments, the method comprises administering one or more IRAKI inhibitor, PDHB inhibitor, or combination thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the invention will be better understood and readily apparent to one of ordinary skill in the art from the following written description, by way of example only, and in conjunction with the drawings, in which:

Figure 1 shows multiple rounds of infection can lead to efficient mycobacterial infection in THP-1 cells. (A) Single round of M. bovis BCG infection at MOI (number of bacterial cells per host cell) ranging from 3:1 to 50:1. Three rounds of M. bovis BCG infection (0, 18, and 42 h) at an MOI of 10:1. Most of THP-1 cells were infected with M. bovis BCG for 72 hours after three rounds of infection (right column). (B-E) The number of BCG-infected THP-1 cells after the single round (B, C and D) and three rounds of infection (E). NS represents not significant. Data represent mean ± SD (n = 3). ^* P<0.05 ^** P<0.01 ^*** P<0.001 .

Figure 2 shows THP-1 cells can be infected with S. flexneri in 3 hours. (A) S. fiexneri infection at an MOI of 10, from 0.5 hour to 5 hours incubation with THP-1 cells. Most of the THP-1 cells were infected after 3 hours incubation. (B-E) The number of Shigella- infected THP-1 cells after 0.5 to 5 hours infection. Most of host cells were killed after 3 and 5 hours infections (D and E). NS represents not significant. N.D represents not detectable. Data represent mean ± SD (n = 3). ^* P<0.05 ^** P<0.01 ^*** P<0.001 .

Figure 3 shows schematic representation of genome-wide CRISPR knockout and CRISPRi screening in human THP-1 cells to identify gene perturbations in mycobacterial and Shigella infections.

Figure 4 shows stable expression of Cas9 and dCas9-KRAB proteins can induce knockout and knockdown of genes in monoclonal THP-1 cells. (A and B) Monoclonal Cas9 and dCas9-KRAB expressing THP-1 cells were obtained by limiting dilution and expanded for an additional 3 weeks. Western blot was performed on whole cell extracts from both wild-type and monoclonal (1 -9) cell lines. Wild-type THP-1 cells, which do not express Cas9 and dCas9- KRAB proteins, were used as negative control. Vinculin was used as a loading control. (C) An sgRNA for EGFP was introduced in both wild-type and Cas9-expressing THP-1 cells using a lentivirus (pXPR-011) that also contains EGFP as a target. Transduction was performed at a low MOI (MOI=0.3). (D) Cas9-expressing THP-1 cells were transduced with an sgRNA targeting AAVS1 at a low MOI. Mutations at AAVS1 locus were detected by SURVEYOR assay. The size of the AAVS1 amplicon is 500 bp. The cleaved product sizes are 320 and 180 bp. (E) Growth measurement associated with sgRNAs targeting INTS9, MCM2, and non- targeting negative controls sgNC1 and sgNC13. (F and G) RT-qPCR analysis of INTS9 and MCM2 transcripts in dCas9-KRAB THP-1 cells. The values are normalized to GAPDH and shown as mean ± SD. ^* P<0.05 ^** P<0.01 ^*** P<0.001 .

Figure 5 shows high quality of CRISPR knockout and CRISPRi screening libraries. (a- c) Coverage of reads in CRISPR knockout libraries. Independent triplicates have <0.0007% undetected guides. The percentage of perfectly matching guides of all triplicates are >86%. The skew ratio of top 10% to bottom 10% of all triplicates are <8.9. (d-f) Distribution of individual sgRNA in control, BCG- and Shigella-infected samples after CRISPR knockout screens. (g-i) Coverage of reads in CRISPRi libraries. Independent triplicates have < 0.00006% undetected guides. The percentage of perfectly matching guides of all triplicates are >80%. The skew ratio of top 10% to bottom 10% of all triplicates are <8.9. (j-l) Distribution of individual sgRNA in control, BCG- and Shigella- infected samples after CRISPRi screens. Each point represents individual sgRNAs. Boxes, 25th to 75th percentile; Whiskers, 1 st to 99th percentile.

Figure 6 shows genome-wide pooled CRISPR knockout and CRISPRi screens dissect biological pathways in mycobacterial and Shigella infections. Enriched genes were filtered with a cut-off of FDR <0.1 and Log2-fold change >1 in mycobacterial infection, a cut-off of FDR <0.25 and Log2-fold change >1 in Shigella infection. Candidate genes were functionally categorized by pathway analysis to understand the biological functions involved in mycobacterial and Shigella infection.

Figure 7 shows top genetic hits identified by secondary CRISPR knockout and CRISPRi screens in mycobacterial infection. (A) Enriched genes were filtered with a cut-off of FDR <0.05 and Log2-fold change >0.5. (B) Validation rate of genes in the secondary CRISPR KO and CRISPRi screens grouped by their p-value in the primary screens. Number of genes per category is indicated. (C) Genetic hits from both primary and secondary screens were ranked by their differential abundance between infection versus uninfected populations (Log2 fold change). (D) Heatmap of screen hits clustered in different biological pathways in mycobacterial infection.

Figure 8 shows top genetic hits identified by secondary CRISPR knockout and CRISPRi screens in Shigella infection. (A) Enriched genes were filtered with a cut-off of FDR <0.05 and Log2-fold change >0.5. (B) Validation rate of genes in the secondary CRISPR KO and CRISPRi screens grouped by their p-value in the primary screens. Number of genes per category is indicated. (C) Genetic hits from both primary and secondary screens were ranked by their differential abundance between infection versus uninfected populations (Log2 fold change). (D) Heatmap of screen hits clustered in different biological pathways in Shigella infection.

Figure 9 shows validation of top positive genetic screen hits and corresponding inhibitors in mycobacterial infection. (A) Correlation of hits between pooled screen and individual validation data. For each hit, the log2 fold change obtained from the genome-wide screening data (Screen phenotype) was plotted against the fold change of cell viability of hits compared to sgNC80 and sgNC135, the non-targeting controls (Validation phenotype). Pearson correlation is indicated. (B) Intracellular M. bovis BCG level after infection of THP-1 cells having individual gene knockdowns (day 14). (C) Schematic of biological pathways and corresponding inhibitors associated with top positive genetic hits. (D)-(F) The growth of THP- 1 cells (D), intracellular mycobacterial growth (E), and production of infection-induced cytokine and chemokines (F) post-infection in the presence or absence of different concentrations of the TYK2 inhibitor cerdulatinib. (G) and (H) The growth of THP-1 cells (G) and intracellular mycobacterial level (H) post-infection in the presence or absence of different concentrations of the AHR inhibitor CH223191. (I) The growth of THP-1 cells and intracellular mycobacterial growth after post-infection treatment with cerdulatinib or CH223191 . Data represent the mean ± SD (n = 3). Two-tailed unpaired Student’s t-test, ^* P<0.05 ^** P<0.01 ^*** P<0.001 ; ns represents not significant.

Figure 10 shows cytokine and chemokine profile in THP-1 cells. (A) and (B) Cytokine and chemokine production of THP-1 cells in the presence or absence of M. bovis BCG infection. (C) Infection-induced cytokine and chemokine production in non-targeting control (NC135 and NC80), IFNAR1 and TYK2 gene knockdown THP-1 cells. Data represent the mean ± SD (n = 3) (two-tailed unpaired Student’s t-test, ^* P<0.05 ^** P<0.01 ^*** P<0.001 ; ns represents not significant).

Figure 11 shows knockdown and inhibit AHR activity regulate metabolism of oxylipins in mycobacterial infection. (A) Diagram of eicosanoids and linoleic acid metabolism in human THP-1 cell line. AHR regulates lipid metabolism by cytochrome P450 (CYP) activation. Metabolites marked in blue represent decreased production in AHR knockdown THP-1 cells or in the presence of CH223191 . Metabolites marked in red represent increased production in the presence of CH223191. (B) CYP-regulated oxylipin production in AHR knockdown and non-targeting control THP-1 cells post-infection. (C) Arachidonic acid production. (D) CYP- regulated eicosanoid production. (E) PGF1a production. (F) LXA4 production. (G) PGE2 production. (H) PGE2/LXA4 ratio. (I)-(L) Increased eicosanoid production in the presence of CH223191 post-infection. (M) and (N) Linoleic acid and CYP-regulated metabolites with or without M. bovis BCG infection and in the presence or absence of CH223191 . Data represent the mean ± SD (n = 5) (two-tailed unpaired Student’s t-test, ^* P<0.05 ^** P<0.01 ^*** P<0.001 ; ns represents not significant).

Figure 12 shows effects of AHR and TYK2 inhibitors on host and M. tuberculosis post infection. (A) Schematic of inhibitor validation in Mtb infection cell models. (B)-(D) Effects of TYK2 inhibitor (B), AHR inhibitor (C), and both inhibitors (“Combo”; D) on growth of M. tuberculosis in THP-1 cells, as measured by the luminescence of the Mtb H37Rv-lux strain. (E) Effects of TYK2 and AHR inhibitors on the survival of primary human monocyte-derived macrophages (hMDM) infected with Mtb H37Rv-lux, with death measured at day 9 by lactate dehydrogenase (LDH) release relative to the untreated condition. (F) Effects of TYK2 and AHR inhibitors on the growth of M. tuberculosis in hMDM cells, as measured by the luminescence of the H37Rv-lux strain relative to the untreated condition. Circles represent data obtained at day 6; squares represent data obtained at day 9. (G) Effects of TYK2 and AHR inhibitors on the growth of M. tuberculosis in hMDM, as measured by total colony-forming units (CFU) of Mtb H37Rv-lux. CFU in the culture supernatant or released after one PBS wash were considered “non-adherent” and marked in deep yellow color, while CFU not released during this wash were considered “adherent” and marked in deep red color. All data represent the mean ± SD (n = 3); imatinib is used at 10 μM as a non-TYK2/AHR inhibitor for comparison in all experiments. Statistics were performed with a two-way results matched ANOVA on time courses (B-D, F) and an ordinary one-way ANOVA for other data (E and G), all followed by Dunnett’s multiple comparisons test. Statistical results are shown only for the final timepoint of growth curves (B-D). Statistics in (G) performed on the sum of both CFU fractions. ^* P<0.05 ^** P<0.01 ^*** P<0.001 ^**** P<0.0001 Figure 13 shows validation of top positive genetic hits and IRAKI inhibitor in S. flexneri infection of human THP-1 cells. (A) Correlation between pooled screen and validation data. For each hit, the log2 fold change obtained from the genome-wide screening data (Screen phenotype) was plotted against the fold-change of cell viability of genetic hits compared to the non-targeting control cells (Validation phenotype). sgNC80 and sgNC135 are non-targeting controls. R is the Pearson correlation coefficient. (B) Intracellular S. /fexner/ level after infection of individual knockdown THP-1 cells. (C) Schematic of positive genetic hits in TLR1/2 signaling pathway and corresponding inhibitors. (D) Cytokine and chemokine production in MYD88 and IRAKI knockdown THP-1 cells post-infection. (E) and (F) The viability of THP-1 cells (E) and intracellular Shigella growth (F) post-infection in the presence or absence of IRAKI inhibitor at different concentrations. Data represent the mean ± SD (n = 3) (two-tailed unpaired Student’s t-test, ^* P<0.05 ^** P<0.01 ^*** P<0.001 ; ns represents not significant).

Figure 14 shows cytokine and chemokine production in THP-1 cells in S. flexneri infection. (A) Cytokine and chemokine production in THP-1 cells with or without S. flexneri infection. (B)-(D), Production of IL-1β (B), IL-2 (C), and IL-8 (D) in THP-1 cells in the presence of IRAKI inhibitor at different concentrations. Data represent the mean ± SD (n = 3) (two-tailed unpaired Student’s t-test, ^* P<0.05 ^** P<0.01 ^*** P<0.001 ; ns represents not significant).

Figure 15 shows validation of IRAKI inhibitor Compound K in Shigella infection. (A) Survival of THP-1 cells post-infection in the presence or absence of different concentrations of Compound K (CK). (B) Intracellular S. flexneri growth in the presence or absence of different concentrations of CK. (C) Production of infection-induced IL-8 in the presence or absence of different concentrations of CK. (D) The growth of THP-1 cells in the presence or absence of CK without S. flexneri infection. Data represent the mean ± SD (n =3) (two-tailed unpaired Student’s t-test, ^* P<0.05 ^** P<0.01 ; ns represents not significant).

Figure 16 shows validation of positive genetic hits in pyruvate catabolism signaling pathway and corresponding inhibitor in S. flexneri infection of human THP-1 cells. (A) Schematic of positive genetic hits in pyruvate catabolism signaling pathway and corresponding inhibitor. (B) and (C) The growth of THP-1 cells (B) and intracellular S. flexneri level (C) post-infection in the presence or absence of different concentrations of PDHB inhibitor (OT) and combined with IRAK1/4-lnh (10 μM). (D) Schematic of inhibitor validation in PMA-stimulated THP-1 cell infected with S. flexneri. (E) Effects of IRAKI and PDHB inhibitors on the survival of differentiated THP-1 post-infection, with death measured by lactate dehydrogenase (LDH) release. (F) Effects of IRAKI and PDHB inhibitors on the growth of intracellular S. flexneri in differentiated THP-1 cells. IRAK1/4-lnh is used at 10 μM. OT is used at 0.1 mM. (G) and (H) Production of acetyl-CoA (G) and succinate (H) with or without S. flexneri infection and in the presence or absence of OT and combined with IRAKI inhibitor. Data represent the mean ± SD (B, C, G and H, n = 3; E and F, n = 4) (two-tailed unpaired Student’s t-test, ^* P<0.05 ^** P<0.01 ^*** P<0.001 ^**** P<0.0001 ; ns represents not significant; N.D represents not detectable).

Figure 17. Validation of inhibitors in S. flexneri infection of primary human macrophages. (A) Schematic of inhibitor validation in S. flexneri infection cell models. (B) Effects of IRAKI and PDHB inhibitors on the survival of primary human monocyte-derived macrophages (hMDM) infected with S. flexneri, with host cell death measured by LDH release. (C) Effects of IRAKI and PDHB inhibitors on the growth of intracellular S. flexneri in hMDM, as measured by CFU. IRAK1/4-lnh was used at 10 μM. Oxythiamine (OT) was used at 0.1 mM. Data represent the mean ± SD (n = 4) (two-tailed unpaired Student’s t-test, ^* P<0.05 ^** P<0.01 ^*** P<0.001 ^**** P<0.0001).

BRIEF DESCRIPTION OF THE TABLE

Exemplary embodiments of the invention will be better understood and readily apparent to one of ordinary skill in the art from the following written description, by way of example only, and in conjunction with the tables, in which:

Table 1 shows genetic hits from the genome-wide CRISPR-Cas9 knockout screen in M. bovis BCG infection.

Table 2 shows genetic hits from the genome-wide CRISPRi screen in M. bovis BCG infection.

Table 3 shows enrichment of biological pathways identified by genome-wide CRISPR KO and CRISPRi screens in mycobacterial infection.

Table 4 shows genetic hits from the genome-wide CRISPR-Cas9 knockout screen in S. flexneri infection.

Table 5 shows genetic hits from the genome-wide CRISPRi screen in S. flexneri infection.

Table 6 shows enrichment of biological pathways identified by genome-wide CRISPR KO and CRISPRi screens in Shigella infection.

Table 7 shows genetic hits from secondary CRISPR-Cas9 knockout and CRISPRi screens in M. bovis BCG infection.

Table 8 shows genetic hits from secondary CRISPR-Cas9 knockout and CRISPRi screens in S. flexneri infection.

Table 9 shows genetic hits and inhibitors validated in M. bovis BCG and S. flexneri infections.

Table 10 shows primers used in Surveyor assay and RT-qPCR analysis. DETAILED DESCRIPTION

Although curable with antibiotics, tuberculosis (TB) and shigellosis remain major causes of morbidity and mortality worldwide. In 2016, 1 .67 and 0.21 million people were killed by TB and shigellosis, respectively (1 , 2). One major factor leading to TB and shigellosis re-emergence is antibiotic resistance in Mycobacterium tuberculosis (Mtb) and Shigella spp., the causative agent of TB and shigellosis. Treatment of multi- and extensively drug-resistant (MDR and XDR) TB is inefficient, toxic, and can last 2 years. Treatment for shigellosis is also becoming increasingly difficult since most of inexpensive and widely used antibiotics are not effective, including ampicillin, amoxicillin, metronidazole, cephalexin, and chloramphenicol. Moreover, antimicrobial resistance to ciprofloxacin, the first-line treatment for shigellosis in children, increased 48.5-fold from 1998 to 2009 in Asia and Africa regions (3). Yet, the development of new antibiotics has declined since the 1980s. Only two anti-TB drugs have been approved by the Food and Drug Administration (FDA) during the past 40 years. In addition, the use of these new antibiotics is likely to be limited by toxicity, and adverse effects to the patients. To overcome many of the current obstacles and improve TB and shigellosis treatment, the present disclosure proposed a fundamentally new treatment approach that targets the human host. This approach can be applied to treat both antibiotic- susceptible and antibiotic-resistant TB and shigellosis.

Host-pathogen interactions are key to bacterial pathogenesis. Macrophages have the ability to recognize, take up, and kill invading pathogens by diverse antimicrobial functions (4). For instance, infected macrophages are able to stimulate the formation of phagolysosomes, which contain antimicrobial proteases and lipases; induce autophagy and apoptosis to degrade pathogens; or trigger granuloma formation, thereby limiting pathogen replication (5). Paradoxically, some pathogens, such as Mtb and Shigella spp., preferentially target host immune cells, such as macrophages. These intracellular pathogens have evolved remarkable abilities to hijack macrophages and manipulate the host cell machinery for their own survival and replication. For instance, Mtb is able to arrest phagosomal maturation, suppress the antibacterial response, stimulate lipid body production, and induce a necrosis response in macrophages (6). On the other hand, after invading macrophages, Shigella disrupts the phagosome vacuole, disseminates into the cytosol, and releases effectors of the type III secretion system (T3SS), which ultimately elicit rapid pyroptotic macrophage death (7).

Surprisingly, only 5 to 10% of individuals infected with Mtb develop active TB disease and more than 65% of all deaths from shigellosis occur in children under 5 years old and people older than 70 years, which indicates that the fully developed and healthy host innate immune response may be sufficient to prevent and control these bacterial infections. Moreover, recent studies suggest that drugs targeting host molecular pathways are able to regulate bacterial pathogenesis and control bacterial infection (8). For instance, the ABL family tyrosine kinase inhibitor imatinib, developed to treat chronic myelogenous leukemia, promotes phagosome acidification and maturation (9). The AMPK-activating anti-diabetic drug metformin controls intracellular Mtb growth, increases the production of reactive oxygen species, and facilitates phagolysosome formation (10). Vitamin D stimulates the production of cathelicidin antimicrobial peptides in macrophages (11). These studies demonstrate the potential of new and existing host-directed therapy (HDT) agents that could improve infectious disease treatment by selectively targeting host pathways. Furthermore, a better understanding of the interaction of the intracellular pathogens with the human host is essential to identifying potential drug targets for HDT.

Considering the complexity of the macrophage-pathogen interactions responsible for intracellular bacterial infections, a high-throughput unbiased genome-wide screening system is needed to systematically identify host perturbations preventing bacterial pathogenesis. RNA interference (RNAi), the dominant genome-wide screening tool in mammals, has been developed to investigate host-mycobacteria interactions and to identify host factors involved in the regulation of mycobacterial infections (12, 13). However, this approach is limited by off-target effects and incomplete suppression of target gene expression, which confound the interpretation of large-scale screens. In contrast, CRISPR (clustered, regularly interspaced, short palindromic repeats)/Cas9 (CRISPR-associated protein 9), a recent revolutionary genome editing technology applicable to mammalian cells, facilitates highly efficient and robust genome-wide screening with minimal off-target effects (14, 15). Pooled lentiviral-based libraries containing single guide RNAs (sgRNAs) that target the whole genome were transduced into cell lines, and specific cellular phenotypes caused by gene knockout were selected based on cell survival and growth. To elucidate how host cells respond to bacterial virulence factors, such as T3SS, lipopolysaccharide, and various toxins, pooled CRISPR loss-of-function screens were conducted in various types of cell lines (16-19). In order to comprehensively study the function of essential genes and long noncoding RNAs of host cells responding to bacterial toxins, CRISPR-mediated repression (CRISPRi) has been developed by fusing enzymatically dead Cas9 (dCas9) to transcriptional suppressor KRAB, and the resulting constructs have been utilized for functional genomic screening (20). These studies demonstrated the great potential of CRISPR screening to dissect the host response to bacterial-mediated cytotoxicity. However, the interactions between intracellular pathogenic bacteria and infected host cell are poorly understood.

In some examples, the present disclosure provides genetically engineered cells for use in genetic screens of human immune cells to identify novel host targets and compounds for infectious disease treatment. That is, the present disclosure relates to a new CRISPR/Cas9-mediated genome-wide forward genetic screening in human immune cells to identify genetic perturbations preventing bacterial pathogenesis, which provides unbiased comprehensive insights into the mechanisms of different types of host-pathogen interactions.

Thus, in the present disclosure, there is provided an immune cell comprising a non- naturally occurring nuclease system capable of excising one or more gene of the cell.

In another example, there is provided an epithelial cell comprising a non-naturally occurring nuclease system capable of excising one or more gene of the cell.

In some examples, the non-naturally occurring nuclease system capable of excising one or more gene of the cell may comprise a genome-targeted nuclease. In some examples, the non-naturally occurring nuclease system may further comprise a nuclease system guide RNA (gRNA).

In some examples, the gRNA may be capable of hybridising or hybridises to a sequence of a DNA molecule in the cell. In some examples, the gRNA and the genome- targeted nuclease may be provided in the cell via the same or different vectors of the system. In some examples, the non-naturally occurring nuclease system may be CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat).

Thus, in one aspect, there is provided an immune cell comprising a CRISPR system comprising a genome-targeted nuclease and/or a guide RNA (gRNA) that hybridizes to a sequence of a DNA molecule in the cell.

In some examples, the genome-targeted nuclease may be any endonuclease suitable for genome engineering (or capable of cleaving a nucleic acid sequence at the appropriate site (e.g. at protospacer adjacent motive (PAM) sequence). In some examples, the endonuclease may be, but is not limited to, Cas9 (such as S. Pyogenes Cas9 and/or Staphylococcus aureus Cas9), dCas9-Krab, Cpf1 (i.e. Cas12a), and/or variants thereof, and/or orthologs thereof, and the like. In some examples, the endonuclease may cleave at PAM sequence (i.e. 5’-NGG-3’) and/or non-NGG PAM sequences. In some examples, the nuclease variant may include, but is not limited to, Cas9-derived variants with non-NGG PAM sequences, xCas9 (which targets broad set of PAM sequences such as NG, GAA, and GAT), Cas9-NG (which recognises NG PAM), and the like.

In some examples, the gRNA may be provided (or delivered) by microinjection, electroporation, lipid nanoparticle, or viral delivery system (such as in a virus particle). In some examples, the gRNA may be provided (or delivered) by a virus particle. In some examples, the gRNA may be provided in suitable delivery system such as viral delivery system including, but is not limited to, a lentiviral gRNA library, an adeno-associated virus (AAV) gRNA library and/or an adenovirus gRNA library, or non-virus delivery methods such as, but is not limited to, microinjection, electroporation, and lipid nanoparticle. In some examples, the gRNA may be provided as a single guide RNA (sgRNA). In some examples, the non-naturally occurring nuclease system may inactivate one or more gene products. In some examples, the non- naturally occurring nuclease system may excise and/or inactivate one or more gene. In some examples, the non-naturally occurring nuclease system may excise and/or inactivate one or more gene and/or gene products related to a system such as, but is not limited to, immune response and/or cell metabolism and/or cell survival and/or cell growth and/or cytotoxicity (for example, bacterial-mediated cytotoxicity).

In some examples, the nuclease system guide RNA may be labelled with a detectable marker. For example, the detectable marker may be a fluorescent label and/or a barcode. In some examples, the fluorescent label may include, but is not limited to green fluorescent protein (GFP), DsRed, or monomeric red fluorescent proteins (mRFPs) such as mCherry, mStrawberry, mOrange, dTomato, and the like. In some examples, the gRNA and/or sgRNA may be labelled with barcode. In some examples, each gRNA or sgRNA may be labelled with a unique barcode.

In some examples, the non-naturally occurring nuclease system may be a CRISPR- Cas9/dCas9-KRAB system. In some examples, the CRISPR-Cas9/dCas9-KRAB system may excise one or more gene based on one or more genome libraries (e.g. CRISPR KO and CRISPRi libraries).

In some examples, the immune cell may be an innate immune cell. In some examples, the immune cell may be a phagocytic immune cell. In some examples, the innate immune cell may include cells that function as professional antigen-presenting cells in an adaptive immunity. In some examples, the immune cell may be selected from the group consisting of a monocyte, a macrophage, a dendritic cell, and an antigen presenting cell. In some examples, the immune cell is a monocytic cell.

In some examples, the immune cell may be an adaptive immune cell. In some examples, the immune cell may be a T lymphocyte, a B lymphocyte, and the like.

In some examples, the immune cell may be an immune cell isolated from a patient (i.e. primary cells from patient). In some examples, the immune cell may be a cultured cell line (i.e. immortalised cell line). In some examples, the immune cell may be a human monocyte cell line. In some examples, the immune cell may be a THP-1 cell and/or a RAW 264.7 cell. In some examples, the immune cell may be a mouse monocyte/macrophage cell line, such as RAW 264.7 cells. In some examples, the immune cell may be a human monocyte cell line, such as THP-1 cell. In some examples, the epithelial cell may be a cell isolated from a patient. In some examples, the epithelial cell may be a cultured cell line (i.e. immortalised cell line). In some examples, the epithelial cell may be, but is not limited to, Hela cell, HT29 cell, and the like.

Using the genetically modified immune cell as disclosed herein, the present disclosure developed high-throughput genome-wide CRISPR knockout and CRISPRi screen platforms in human monocytic leukemia THP-1 cells that can be extensively used to identify monocyte or macrophage targets and investigate host functions in different innate immune disease models. The disclosure herein can be used to develop host-directed therapy for different infectious diseases and to identify drug inhibitors for diseases of innate immunity, such as inflammation, neurodegenerative and autoimmune Diseases.

Therefore, also disclosed is a method of conducting a genetic screen of the effect of a pathogen on an immune cell, comprising providing an immune cell as described herein, contacting the immune cell to the pathogen, identifying genetic changes in the immune cell upon contact with the pathogen to thereby identify genes modulation in the immune cell.

Also disclosed is a method of screening a host cell and a pathogen interaction, comprising providing a host cell comprising a non-naturally occurring nuclease system capable of excising one or more gene of the cell contacting the host cell to a pathogen; identifying genetic changes in the host cell on contact with the pathogen to thereby identify genes modulation in the host cell and pathogen interaction.

Also disclosed is a method of conducting a genetic screen/drug screen of the effect of a candidate drug on a host cell, comprising providing a host cell comprising a non-naturally occurring nuclease system capable of excising one or more gene of the cell; contacting the host cell to the candidate drug, identifying genetic changes in the host cell upon contact with the drug to thereby identify genes modulation in the host cell.

In some examples, the step of identifying genetic changes can be performed using apparatus, assays or utilities known in the art. As used herein, the term “genetic changes” refers to the relative difference of expression of a gene with or without (in the presence or absence) contact to an external stimulus (for example to a pathogen and/or a compound/drug/agent of interest). The changes may include a decrease and/or an increase of the expression of a particular gene.

Also disclosed is a method for identifying a compound/drug/agent that enhances an immune response/enhances a defence response to a pathogen, comprising providing an immune cell according to any of the preceding AS or a host cell comprising a non-naturally occurring nuclease system capable of excising one or more gene of the cell, contacting the immune cell or the host cell to the pathogen in absence or presence of the compound/drug/agent, and determining the responses elicited in the immune cell or the host cell to the pathogen in the presence of compound/drug/agent relative to the responses elicited in the immune cell or host cell to the pathogen in the absence of the compound/drug/agent.

In another aspect, there is provided a method for identifying an agent that enhances an immune response to a pathogen, comprising providing an immune cell or a host cell comprising CRISPR system comprising a genome-targeted nuclease and/or a gRNA that hybridizes to a sequence of a DNA molecule in the cell, contacting the immune cell or the host cell to the pathogen in absence or presence of the agent, and determining the responses elicited in the immune cell or the host cell to the pathogen in the presence of the agent relative to the responses elicited in the immune cell or host cell to the pathogen in the absence of the agent.

In some examples, the pathogen may be a microorganism. In some examples, the microorganism may be an infectious microorganism. In some examples, the microorganism may include, but is not limited to, bacteria, virus, fungi, algae, protozoa, and the like. In some examples, the microorganism may be a drug-resistant and/or a drug-susceptible microorganism. In some examples, the microorganism may be a drug-resistant and/or a drug- susceptible bacterium. In some examples, the microorganism may be an antibiotic-resistant and/or an antibiotic-susceptible bacterium. In some examples, the pathogen may be a bacterium. In some examples, the bacterium may include, but is not limited to, Aerobacter, Aeromonas, Acinetobacter, Agro bacterium, Bacillus, Bacteroides, Bartonella, Bordetella, Bortella, Borrelia, Brucella, Burkholderia, Calymmatobacterium, Campylobacter, Citrobacter, Clostridium, Cornyebacterium, Coxiella, Enterobacter, Enterococcus, Escherichia, Francisella, Gardnerella, Haemophilus, Hafnia, Helicobacter, Klebsiella, Legionella, Listeria, Morganella, Moraxella, Mycobacterium, Neisseria, Pasteurella, Proteus, Providencia, Pseudomonas, Salmonella, Serratia, Shigella, Staphylococcus, Streptococcus, Stentorophomonas, Treponema, Xanthomonas, Vibrio, and Yersinia. In some examples, the bacterium may be Mycobacterium or Shigella. In some examples, the pathogen may be include, but is not limited to, Mycobacterium tuberculosis (MTB), Mycobacterium bovis (strain bacillus Calmette-Guerin (BCG)), Salmonella enterica, Salmonella bongori, Listeria monocytogenes, Legionella spp (such as Legionella pneumophila), Shigella flexneri, and the like.

The inventors of the present disclosure found that the methods as described herein would be useful in identifying potential targets of drug-resistant pathogens. Thus, in some examples, the pathogen may be a drug-resistant bacterium. In some examples, the pathogen may be an antibiotic-resistant and/or an antibiotic-susceptible bacterium.

In some examples, the non-naturally occurring nuclease system capable of excising one or more gene of the cell may comprise a genome-targeted nuclease. In some examples, the non-naturally occurring nuclease system may further comprise a nuclease system guide RNA (gRNA). In some examples, the gRNA may be provided as a single guide RNA (sgRNA). In some examples, the gRNA may be capable of hybridising or hybridises to a sequence of a DNA molecule in the cell. In some examples, the gRNA and the genome-targeted nuclease may be provided in the cell via the same or different vectors of the system.

In some examples, the non-naturally occurring nuclease system may be CRISPR. In some examples, the genome-targeted nuclease may be any endonuclease suitable for genome engineering (or capable of cleaving a nucleic acid sequence at the appropriate site (e.g. at protospacer adjacent motive (PAM) sequence) such as, but is not limited to, Cas9 (such as Streptococcus Pyogenes Cas9 and/or Staphylococcus aureus Cas9), dCas9-Krab, Cpf1 (i.e. Cas12a), and/or variants thereof, and/or orthologs thereof, and the like. In some examples, the endonuclease may cleave at PAM sequence (i.e. 5’-NGG-3’) and/or non-NGG PAM sequences. In some examples, the nuclease variant may include, but is not limited to, Cas9-derived variants with non-NGG PAM sequences, xCas9 (which targets broad set of PAM sequences such as NG, GAA, and GAT), Cas9-NG (which recognises NG PAM), and the like. In some examples, the gRNA may be provided (or delivered) by microinjection, electroporation, lipid nanoparticle, or in a virus particle. In some examples, the gRNA may be provided (or delivered) by a virus particle. In some examples, the gRNA may be provided in suitable delivery system such as viral delivery system including, but is not limited to, a lentiviral gRNA library, an adeno-associated virus (AAV) gRNA library and/or an adenovirus gRNA library, or non-virus delivery methods such as, but is not limited to, microinjection, electroporation, and lipid nanoparticle.

In some examples, the nuclease system guide RNA may be labelled with a detectable marker. For example, the detectable marker may be a fluorescent label and/or a barcode. In some examples, the fluorescent label may include, but is not limited to green fluorescent protein (GFP), DsRed, or monomeric red fluorescent proteins (mRFPs) such as mCherry, mStrawberry, mOrange, dTomato, and the like. In some examples, the gRNA and/or sgRNA may be labelled with labelled with barcode. In some examples, each gRNA or sgRNA may be labelled with a unique barcode.

In some examples, the non-naturally occurring nuclease system may inactivate one or more gene products. In some examples, the non-naturally occurring nuclease system may excise and/or inactivate one or more gene. In some examples, the non-naturally occurring nuclease system may excise and/or inactivate one or more gene and/or gene product related (or responsible for) a system such as immune response, cell metabolism, cell survival, cell growth, cytotoxicity (for example, bacterial-mediated cytotoxicity), and the like.

In some examples, the non-naturally occurring nuclease system may be a CRISPR- Cas9/dCas9-KRAB system. In some examples, the CRISPR-Cas9/dCas9-KRAB system may excise and/or inactivate one or more gene based on one or more genome libraries (e.g. CRISPR KO and CRISPRi libraries).

In some examples, the immune cell may be an epithelial cell. In some examples, the epithelial cell may be a cell isolated from a patient. In some examples, the epithelial cell may be a cultured cell line (i.e. immortalised cell line). In some examples, the epithelial cell may be, but is not limited to, Hela cell, HT29 cell, and the like.

In some examples, the immune cell/host cell is an innate immune cell. In some examples, the immune cell/host cell is an adaptive immune cell. In some examples, the immune cell may be a T lymphocyte, a B lymphocyte, and the like.

In some examples, the immune cell may be an innate immune cell. In some examples, the immune cell may be a phagocytic immune cell. In some examples, the innate immune cell may include cells that function as professional antigen-presenting cells in an adaptive immunity. In some examples, the immune cell may be selected from the group consisting of a monocyte, a macrophage, a dendritic cell, and an antigen presenting cell. In some examples, the immune cell is a monocytic cell.

In some examples, the immune cell may be an adaptive immune cell. In some examples, the immune cell may be a T lymphocyte, a B lymphocyte, and the like.

In some examples, the immune cell may be an immune cell isolated from a patient. In some examples, the immune cell may be a cultured cell line (i.e. immortalised cell line). In some examples, the immune cell may be a human monocyte cell line. In some examples, the immune cell may be a THP-1 cell and/or RAW 264.7. In some examples, the immune cell may be a mouse monocyte/macrophage cell line, such as RAW 264.7 cells. In some examples, the immune cell may be a human monocyte cell line, such as THP-1 cell.

In some examples, the immune cell/ host cell may be a cell from a subject having a disease or disorder. In some examples, the disease or disorder may be diseases of innate immunity (such as inflammation), neurodegenerative disease and autoimmune disease.

In some examples, the method may further comprise validation using two or more CRISPR libraries. It is understood that such CRISPR libraries are commercially available and may be obtained from repositories known in the art. For example, CRISPR KO library and CRISPRi library referred to in the present disclosure refer to the Human CRISPR knockout pooled library (Brunello) and the human CRISPRi pooled library (Dolcetto) that were developed by John Doench (the Broad Institute), respectively. As such, in some example, the CRISPR KO library and CRISPRi library may be obtained from Addgene (a non-profit plasmid repository, Massachusetts, USA) as Brunello (#73178) and Dolcetto (#92385), respectively. In some examples, the CRISPR KO library (i.e. CRISPRko library) may be obtained from the Broad Institute, for example at https://www.addgene.org/pooled-library/broadgpp-human- knockout-brunello/; and the CRISPRi library may be obtained from the Broad Institute, for example at https://www.addgene.org/pooled-library/broadgpp-human-crispr i-dolcetto/.

In some examples, the method as disclosed herein may further comprise the use of one or more secondary CRISPR screen library. In some examples, the secondary CRISPR screen library may be designed by publicly or commercially available guide RNA designers. In some examples, the secondary CRISPR library may be designed by gRNA (such as sgRNA) designers such as, but is not limited to, GPP sgRNA Designer developed by John Doench of The Broad Institute (at https://portals.broadinstitute.org/gpp/public/analysis- tools/sgrna-design-crisprai).

In some examples, the present disclosure also provides genome-wide CRISPR knockout (KO) and CRISPRi screen systems in human monocytic THP-1 cells to systematically investigate complex host-pathogen interactions. In some examples, the present disclosure also provides genome wide CRISPR knockout and CRISPRi screening platform that is capable of identifying new potential therapeutic host targets and corresponding selective inhibitors in a high-throughput way with high sensitivity, specificity and accuracy for infectious disease treatment such as tuberculosis and shigellosis treatment.

Here, as shown in the Examples section, the inventors have established pooled genome-wide CRISPR knockout (KO) and CRISPRi screen systems in human monocytic THP-1 cells to systematically investigate complex host-pathogen interactions based on host cell survival. The inventors have shown how the genetically modified immune cell as described herein can be used to discover new host targets that are essential for enhanced macrophage immune responses and inhibited intracellular pathogen growth and replication. Moreover, the inventors have identified host gene targets which are likely to be inhibited by existing drug inhibitors to prevent bacterial pathogenesis.

The present disclosure provides for tools to comprehensively dissect biological pathways in human immune cells for modulating host-pathogen interactions in an unbiased manner. The pooled genome wide CRISPR knockout and CRISPRi screening platforms as provided herein are able to identify multiple host targets in bacterial infections with lower off-target effects compared with RNAi screen. More importantly, the present disclosure identified new potential therapeutic host targets and corresponding selective inhibitors for tuberculosis and shigellosis treatment. Host-directed therapy is less likely to induce antibiotic resistance than traditional antimicrobial treatment directly targeting bacterial pathogens and may decrease inflammatory tissue damage and active protective immune responses in host. Moreover, repurposing existing drug inhibitors which are utilized for other diseases can save cost and time for drug development. In some examples, the compound/drug/agent may be provided in a compound library. In some examples, the compound/drug/agent may be a small organic compound. In some examples, the compound/drug/agent may be a peptide, peptidomimetic, or an antibody or fragment thereof.

In general, novel compound/agent/drug are identified from large libraries of both natural product or synthetic (or semi-synthetic) extracts or chemical libraries according to methods known in the art. The screening method of the present disclosure is appropriate and useful for testing compound/agent/drugs from a variety of sources for possible immune enhancing or defence enhancing activity. The initial screens may be performed using a diverse library of compounds, but the method is suitable for a variety of other compounds and compound libraries. Such compound libraries can be combinatorial libraries, natural product libraries, or other small molecule libraries. In addition, compounds from commercial sources can be tested, as well as commercially available analogues of identified inhibitors.

For example, those skilled in the field of drug discovery and development will understand that the precise source of test extracts or compounds is not critical to the screening procedure(s) of the invention. Accordingly, virtually any number of chemical extracts or compounds can be screened using the methods described herein. Examples of such extracts or compounds include, but are not limited to, plant-, fungal-, prokaryotic- or animal-based extracts, fermentation broths, and synthetic compounds, as well as modification of existing compounds. Numerous methods are also available for generating random or directed synthesis (e.g., semi-synthesis or total synthesis) of any number of chemical compounds, including, but not limited to, saccharide-, lipid-, peptide-, and nucleic acid-based compounds. Synthetic compound libraries are commercially available. Alternatively, libraries of natural compounds in the form of bacterial, fungal, plant, and animal extracts are commercially available from a number of sources known in the art. In addition, natural and synthetically produced libraries are produced, if desired, according to methods known in the art, e.g., by standard extraction and fractionation methods. Furthermore, if desired, any library or compound is readily modified using standard chemical, physical, or biochemical methods.

In addition, those skilled in the art of drug discovery and development readily understand that methods for dereplication (e.g., taxonomic dereplication, biological dereplication, and chemical dereplication, or any combination thereof) or the elimination of replicates or repeats of materials already known for their anti-pathogenic activity should be employed whenever possible.

When a crude extract is found to have activity that promotes or enhances a host's defence (or immune response) to a pathogen, further fractionation of the positive lead extract is necessary to isolate chemical constituents responsible for the observed effect. Thus, the goal of the extraction, fractionation, and purification process is the careful characterization and identification of a chemical entity within the crude extract having anti-pathogenic activity. Methods of fractionation and purification of such heterogenous extracts are known in the art. If desired, compounds shown to be useful agents for the promoting or enhancing a host defence response are chemically modified according to methods known in the art.

Since many of the compounds in libraries such as combinatorial and natural products libraries, as well as in natural products preparations, are not characterized, the screening methods of this present disclosure provide novel compounds which are active as inhibitors or inducers in the particular screens, in addition to identifying known compounds which are active in the screens. Therefore, this present disclosure includes such novel compounds, as well as the use of both novel and known compounds in pharmaceutical compositions and methods of treating.

The methods of the present disclosure provide a simple means for identifying host/immune factors and genes that enable a host cell / immune cell to combat a pathogen and compounds capable of either inhibiting pathogenicity or enhancing a host's (or immune cells’) resistance capabilities to such pathogens. Accordingly, a chemical entity discovered to have medicinal value using the methods described herein are useful as either drugs, or as information for structural modification of existing anti-pathogenic compounds, e.g., by rational drug design.

For therapeutic uses, the compositions or agents identified using the methods disclosed herein may be administered systemically, for example, formulated in a pharmaceutically acceptable buffer such as physiological saline. Routes of administration may include, for example, subcutaneous, intravenous, interperitoneally, intramuscular, or intradermal injections which provide continuous, sustained levels of the drug in the patient. Treatment of human patients or other animals will be carried out using a therapeutically effective amount of an anti-pathogenic agent in a physiologically acceptable carrier.

In other words, the present disclosure relates to a new high-throughput genetic screening method for the identification of host immune cell targets in infectious diseases such as tuberculosis and shigellosis. The method as disclosed herein utilizes genome-wide CRISPR knockout and CRISPRi screening in human monocytic THP-1 cells with bacterial pathogens. The method as disclosed herein investigates the complex host-pathogen interactions for identifying genetic perturbations preventing bacterial pathogenesis using sequenced sgRNA barcodes from THP-1 cells without bacterial infection as control. The CRISPR knockout and CRISPRi screen libraries are used to validate genome-wide genetic hits and validates host targets and drug inhibitors in mycobacterial and Shigella infections. The identification/validation of host targets from CRISPR screens helps to develop host-directed therapy/ treatment for bacterial infections. The method as described herein treats both antibiotic-susceptible and antibiotic-resistant TB and shigellosis. This is because, the present disclosure conductsCRISPR/Cas9-mediated genome-wide forward genetic screens in human immune cells to identify genetic perturbations preventing bacterial pathogenesis. The genetic screens provided unbiased comprehensive insights into the mechanisms of different types of host-pathogen interactions.

As such, in yet another aspect, there is provided a method of treating a bacterial infection in a subject in need thereof, comprising administering one or more modulator of one or more targets referred to in one or more tables selected from the group consisting of Table 1 , Table 2, Table 4, Table 5, Table 7A, Table 7B, Table 8A, Table 8B, and Table 9 into the subject. In some embodiments, the method of treating bacterial infection as disclosed herein comprises one or more modulator of one or more targets having a nucleic acid sequence including, but is not limited to, SEQ ID NOs: 9 to 3691 .

As shown in the Examples section, the present disclosure has illustrated how the present disclosure can be used to identify host targets in a high-throughput way with high sensitivity, specificity and accuracy for infectious disease treatment. As would be apparent to the person skilled in the art, the platforms as provided herewith can be utilized to identify genetic hits for many other bacterial infections. To provide an illustration on the utility of the present disclosure, the Examples section showed how the inventors identified and validated specific host targets with high therapeutic potential for tuberculosis and shigellosis treatment (Table 9), especially TYK2, AHR, YPEL5, and WDR26 in mycobacterial infection and IRAK1/4, PDHB, and TRAF6 in Shigella infection. Moreover, the inventors have also identified and validated inhibitors with great therapeutic potential as host-directed therapy for tuberculosis and shigellosis treatment, such as Cerdulatinib, CH223191 , IRAK1/4-lnh, Ginsenoside Compound K, and oxythiamine. Thus, the present disclosure has demonstrated possible applications in the field of infectious diseases for host-directed therapy.

As such, also disclosed is a method of treating a mycobacterial infection in a subject in need thereof, comprising administering one or more modulator of one or more targets referred to in one or more of Table 1 , Table 2, Table 7A, Table 7B, or Table 9 into the subject. In some examples, there is provided the use of one or more modulator of one or more targets referred to in Table 9 in the manufacture of a medicament for treating a mycobacterial infection in a subject in need thereof. The one or more modulator of one or more targets referred to in Table 9 may be used in treating a mycobacterial infection in a subject in need thereof. In some examples, the one or more targets may comprise or consist of nucleic acid sequence including, but is not limited to sequences referred to in Table 9, for example, SEQ ID NO: 578 or 2905 or 3392, SEQ ID NO: 775, SEQ ID NO: 593 or 2918 or 3568, SEQ ID NO: 575, SEQ ID NO: 581 , SEQ ID NO: 608 or 3051 , SEQ ID NO: 745 or 2465 or 3667, SEQ ID NO: 624 or 2429 or 3021 , SEQ ID NO: 640 or 3002, SEQ ID NO: 596, SEQ ID NO: 611 or 2402 or 3127, SEQ ID NO: 602, SEQ ID NO: 620 or 2477 or 3016 or 3582, SEQ ID NO: 830 or 2992 or 3464, SEQ ID NO: 792 or 2384 or 3415, SEQ ID NO: 779 or 2387 or 3687, SEQ ID NO: 2393 or 3613, SEQ ID NO:3689, SEQ ID NO: 2408, SEQ ID NO: 2414, SEQ ID NO: 578 or 2905 or 3392, SEQ ID NO: 2399 or 3474, SEQ ID NO: 624 or 2429 or 3021 , SEQ ID NO: 2432, SEQ ID NO: 620 or 2477 or 3016 or 3582, SEQ ID NO: 630 or 2390 or 3092 or 3654, SEQ ID NO: 830 or 2992 or 3464, SEQ ID NO: 3690, and/or SEQ ID NO: 3691 . In some examples, wherein the infection is a mycobacterial infection, the modulator may activate or inhibit one or more targets selected from the group consisting of i. Type I interferon pathway (such as, but is not limited to, TYK2, IFNAR1 , IFNAR2, STAT2, JAK1 , and IRF9); ii. Lipid metabolism pathway (such as, but is not limited to, AHR, ARNT, HELZ2, and PITPNB); iii. PI3K/AKT pathway (such as, but is not limited to, USP7, IRAKI , and CHUK); iv. Innate immunity pathway (such as, but is not limited to, YPEL5, MEM01 , and UBA7); v. Apoptosis pathway (such as, but is not limited to, WDR26, BAX, and BAK1 ); vi. TCA cycle pathway (such as, but is not limited to, NNT); vii. Other signalling pathways (PHIP and TRERF1). In some examples, the inhibitor of TYK2 and JAK1 may be Cerdulatinib. In some examples, the inhibitor of AHR may be CH223191. In some examples, the method may comprise administering an effective amount of Cerdulatinib and/or CH223191 into the subject.

Also disclosed is a method of treating a Shigella infection in a subject in need thereof, comprising administering one or more modulator of one or more targets referred to in one or more of Table 4, Table 5, Table 8A, Table 8B, or Table 9 into the subject. In some examples, there is provided the use of one or more modulator of one or more targets referred to in Table 9 in the manufacture of a medicament for treating a Shigella infection in a subject in need thereof. The one or more modulator of one or more targets referred to in Table 9 may be used in treating a Shigella infection in a subject in need thereof. In some examples, wherein the infection is a Shigella infection, the modulator may activate or inhibit one or more targets selected from the group consisting of i. Chromatin modifying pathways (such as, but is not limited to, MSL2, KMT2D, KMT2C, and KDM4A); ii. Toll-like receptor signalling (such as, but is not limited to, TRAF6, TIFA, IRAKI and MYD88); iii. Pyruvate metabolism (such as, but is not limited to, PDHB, DLAT, PDHA1 CS, MPC1 , and MPC2); iv. genes involved in antimicrobial peptides production pathways (such as, but is not limited to, HDAC2, and CREBBP); v. PIP3/AKT signalling pathways (such as, but is not limited to, LAMTOR3, MAP2K7, MAPK9 and LAMTOR2). In some examples, the modulator may activate or inhibit one or more target selected from the group consisting of IRAKI /4, PDHB, MSL2, and TRAF6. In some examples, the modulator may be an inhibitor of PDHB and/or IRAKI into the subject. In some examples, the inhibitor of PDHB may be oxythiamine (OT). In some examples, the inhibitor of IRAKI may be IRAK1/4-lnh, Ginsenoside Compound K (CK). In some examples, the method may comprise administering one or more IRAKI inhibitor, PDHB inhibitor, or combination thereof.

Further, in the description herein, the word “substantially” whenever used is understood to include, but not restricted to, "entirely" or “completely” and the like. In addition, terms such as "comprising", "comprise", and the like whenever used, are intended to be non- restricting descriptive language in that they broadly include elements/components recited after such terms, in addition to other components not explicitly recited. For an example, when “comprising” is used, reference to a “one” feature is also intended to be a reference to “at least one” of that feature. Terms such as “consisting”, “consist”, and the like, may, in the appropriate context, be considered as a subset of terms such as "comprising", "comprise", and the like. Therefore, in embodiments disclosed herein using the terms such as "comprising", "comprise", and the like, it will be appreciated that these embodiments provide teaching for corresponding embodiments using terms such as “consisting”, “consist”, and the like. Further, terms such as "about", "approximately" and the like whenever used, typically means a reasonable variation, for example a variation of +/- 5% of the disclosed value, or a variance of 4% of the disclosed value, or a variance of 3% of the disclosed value, a variance of 2% of the disclosed value or a variance of 1% of the disclosed value.

Furthermore, in the description herein, certain values may be disclosed in a range. The values showing the end points of a range are intended to illustrate a preferred range. Whenever a range has been described, it is intended that the range covers and teaches all possible sub-ranges as well as individual numerical values within that range. That is, the end points of a range should not be interpreted as inflexible limitations. For example, a description of a range of 1% to 5% is intended to have specifically disclosed sub-ranges 1% to 2%, 1% to 3%, 1% to 4%, 2% to 3% etc., as well as individually, values within that range such as 1%, 2%, 3%, 4% and 5%. The intention of the above specific disclosure is applicable to any depth/breadth of a range.

It will be appreciated by a person skilled in the art that other variations and/or modifications may be made to the specific embodiments without departing from the scope of the invention as broadly described. For example, in the description herein, features of different exemplary embodiments may be mixed, combined, interchanged, incorporated, adopted, modified, included etc. or the like across different exemplary embodiments. The present embodiments are, therefore, to be considered in all respects to be illustrative and not restrictive.

EXPERIMENTAL SECTION Materials and Methods Reagents and antibodies

Cerdulatinib and CH223191 were purchased from Cayman chemical and used at the following concentrations: Cerdulatinib 0.001-10 μM, CH223191 0.03-3 μM. IRAK1/4-lnh (I5409) and ginsenoside Compound K were purchased from Sigma and used at the following concentrations: IRAK1/4-lnh 0.1-10 μM, Compound K 1-25 μM. Oxythiamine was used at 0.01-1 mM. The antibodies used were: anti-CRISPR-Cas9 monoclonal antibody 7A9-3A3 (Abeam), Vinculin monoclonal antibody (Enzo), and goat anti-mouse IgG HRP conjugate (Thermo Scientific). Antibiotics in the media were at following concentrations: 100 μg ml ^-1 ampicillin, 25 μg ml ^-1 kanamycin, 100 μg ml ^-1 gentamicin, 100 U ml ^-1 Penicillin-Streptomycin.

Mammalian cell culture

The human monocyte cell line THP-1 cells were gifts from Jianzhu Chen (Singapore- MIT Alliance for Research and Technology). HEK293FT cells were gifts from Asha Shekaran (Engine Biosciences). THP-1 cells were cultured in RPMI1640 (HyClone) with 10% FBS (Gibco) and Pen/Strep (Gibco) at 37°C with 5% CO ₂ . HEK293FT cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) (HyClone) supplemented with 10% FBS and Pen/Strep at 37°C with 5% CO ₂ .

Frozen peripheral blood mononuclear cells were obtained by Ficoll gradient centrifugation of healthy donor leukaphereses (Research Blood Components). Primary human monocytes were isolated by CD14 positive selection (Stemcell Technologies). Monocytes were allowed to mature into macrophages on tissue culture-treated dishes using 50 ng ml ^-1 GM-CSF (BioLegend) for 6 days in RPMI1640 with 10% FBS, 10 mM HEPES, and 1 x GlutaMAX (Gibco). Matured macrophages were dissociated with Accutase (Innovative Cell Technologies), counted, distributed in a 96-well plate format, and allowed to adhere overnight in the same media without GM-CSF. All incubations were performed at 37°C with 5% CO ₂ .

Bacterial strains and growth conditions

Mycobacterium bovis BCG or Mtb H37Rv-lux was grown in Middlebrook 7H9 medium supplemented with 10% albumin-dextrose-catalase (ADC), 0.5% glycerol and 0.05% Tween- 80 at 37°C to an absorbance (A600) of 0.2-0.3. A green fluorescent protein (GFP)-reporter BCG was generated by transformation with plasmid Pmap24::GFP, a gift from Jennifer A. Philips (Washington University School of Medicine in St. Louis), to assess mycobacterial infection. Shigella flexneri M90T ΔvirG pCKI OO (PuhpT::dsRed), a gift from Cecile Arrieumerlou (Institut Cochin), was grown in Lysogeny broth (LB) at 37°C with shaking to exponential phase for infection of cultured host cells. When necessary, 25 μg ml ^-1 kanamycin or 100 μg ml ^-1 ampicillin were added to the growth medium.

In vitro bacterial infection

Frozen M. bovis BCG with Pmap24::GFP were thawed and grown in 7H9 medium to an absorbance at a wavelength 600nm of 0.2-0.3 to prevent bacterial clumps formation (0.3 OD corresponds to ~ 5 x10 ⁷ bacterial cells). THP-1 cells were infected with BCG strains at indicated MOIs (3, 10 and 50) and incubated at 37°C with 5% CO ₂ . After 24 hours, infected host cells were washed twice with 1 xPBS and resuspended with complete RPMI medium containing 100 μg ml ^-1 gentamicin for 2 hours to kill extracellular bacteria. Then, THP-1 cells were centrifuged, washed with 1 xPBS and maintained in RPMI1640 medium. For 3 rounds of BCG infection, THP-1 cells were infected once a day for 3 days at an MOI of 10 and extracellular bacteria were removed 24 hours after the third infection. Subsequently, host cells were washed and maintained for the rest of the experiments. The number of viable THP-1 cells were counted in a hemocytometer by using trypan blue (Gibco).

Prior to infection, bacterial clumps were removed from Mtb H37Rv-lux cultures by “soft spin” centrifugation of PBS-washed bacteria in cell culture media at 121 xg with no brake, after which the top half of the supernatant was isolated and used. Primary macrophages and adherent THP-1 cells were infected with Mtb H37Rv-lux at an MOI of 2 for 7 hours, followed by 3 washes with 1 xPBS and incubation in RPMI with 10% FBS, 25 mM HEPES, 1 x GlutaMAX, and indicated compounds in 0.1% DMSO. Infection was carried out for 7.5-9 days; all incubations were at 37°C with 5% CO ₂ . Luminescence was measured on a BioTek Synergy H1 microplate reader. For cell death measurement by LDH assay (Takara), culture supernatants were clarified with a 5 minute spin at 400xg, added at a 5-fold dilution in 1 xPBS to the working solution, and incubated for approximately 1 hour before absorbance measurement at 492nm and 620nm on the same plate reader.

S. flexneri M90T AvirG pCKIOO were prepared in exponential growth phase for host cell infection. THP-1 cells were infected at an MOI of 10 in complete RPMI1640 medium at indicated times. To test the combination of IRAKI and PDHB small molecule inhibitors, THP- 1 cells and primary human monocyte-derived macrophages (hMDM) were infected at an MOI of 1 :10. After Shigella infection, THP-1 cells and hMDM were treated with 100 μg ml ^-1 gentamicin for 2 hours to kill extracellular bacteria. Subsequently, host cells were washed and maintained for the rest of the experiments. Viable THP-1 cells were counted in a hemocytometer by using trypan blue (Gibcon). Cell death of PMA-stimulated THP-1 cells and hMDM were measured by LDH assay (Takara).

Enumeration of intracellular bacteria in infected cells After BCG infection, 1 ml of the infected THP-1 cells were centrifuged and washed twice with 1xPBS and then lysed with 50 mI of 1xPBS with 1% Triton 100. 10-fold serial dilutions were performed followed by plating on 7H10 agar plates and incubated at 37°C for 3-4 weeks. The number of viable intracellular bacteria was calculated from manually enumerated colony forming units (CFU) on the agar plates.

After Mtb infection, non-adherent bacteria were isolated by combining supernatant and a single 1 xPBS wash, while adherent bacteria were those remaining in the well following this wash. Both samples were lysed with 0.1% Triton 100 for at least 5 minutes and diluted 10-fold in 7H9 media followed by 2-fold dilution in 1 xPBS with 0.05% Tween-80. Dilutions were plated on 7H11 agar plates and incubated at 37°C for approximately 2 weeks.

At selected time points, 1 ml of Shigella- infected THP-1 cells were centrifuged and washed twice with 1xPBS and then lysed with 50 mI of 1xPBS with 1% Triton 100. PMA- stimulated THP-1 cell and hMDM infected with S. flexneri were lysed with 50 mI of 1xPBS with 1% Triton 100. 10-fold serial dilutions were performed followed by plating on LB agar plates and incubated at 37°C for 24 hours. The number of viable intracellular bacteria was calculated from the counted CFU on the agar plates.

Generation of Cas9-expressing and Cas9-Krab-expressing THP-1 cell lines

The plasmid lentiCas9-Blast (Addgene #52962) with human codon-optimized sequence Cas9 (SpCas9) was transduced into THP-1 cells to construct Cas9-expressing THP-1 cell line, which were subsequently expanded in the presence of 10 μg ml ^-1 blasticidin. The plasmid pHR-SFFV-dCas9-BFP-KRAB (Addgene #46911 ) was transduced into THP-1 cells. dCas9-KRAB-expressing THP-1 cells which produce blue fluorescent protein (BFP) were subsequently collected by BD FACS Aria II cell sorter. Monoclonal Cas9 and dCas9- KRAB expressing THP-1 cells, obtained by limiting dilution, served as the parental cell line harboring the human genome-wide CRISPR-Cas9 knockout and CRISPRi libraries, respectively.

Pooled Genome-wide and secondary CRISPR Screens

Human CRISPR knockout pooled library (Brunello) was obtained from Addgene (#73178). Human CRISPRi pooled library (Dolcetto) was a gift from John Doench (the Broad Institute, also available on Addgene #92385). For the secondary screens, the present disclosure designed CRISPR knockout and CRISPRi libraries targeting 251 of genes (169 of genes identified in mycobacterial infection and 133 of genes identified in Shigella infection) scored in primary genome-wide screens, 121 of genes from literatures (86 of genes involved in mycobacterial infection and 47 of genes involved in Shigella infection), and 1000 non- targeting sgRNAs with 10 sgRNAs per gene. Lentiviral library packaging

Seed well-dissociated HEK293FT cells in T175 tissue culture flasks 24 h before transfection in a total volume of 35 ml of DMEM medium at a density of 1 .4x10 ⁷ cells per flask. Cells are optimal for transfection at 80-90% confluency using 210 μL of Lipofectamine 2000, 231 μL of PLUS reagent, 7 ml of Opti-MEM, and a DNA mixture of 11.9 μg of μMD2.G (Addgene #12259), 18.2 μg of psPAX2 (Addgene #12260), and 23.8 μg of library plasmid. Flasks were incubated 37°C with 5% CO ₂ for 4 hours. The media was replaced with 35 ml DMEM medium with 1% BSA and 10% FBS. Lentivirus was harvested 2 or 3 days after the start of transfection and filtered through a 0.45 pm polyethersulfone membrane.

Lentivirus transduction

Cas9-expressing and dCas9-Krab-expressing THP-1 cells were transduced with the pooled lentiviral CRISPR knockout and CRISPRi libraries in three biological replicates at an MOI of 0.3 to ensure that only one gene was targeted in each cell line, respectively. To ensure each perturbation will be fully represented and reduce spurious effects due to random genome integration in the transduced cell population, screening libraries were prepared with coverage of >500 cells per sgRNA. Lentiviral spinfection was performed by centrifuging 12-well plates at 1 ,000 g for 2 hours at 33°C with THP-1 cells grown in RPMI1640 medium with 10% FBS and 8 μg/ml of polybrene. 24 hours after lentiviral transduction, cell culture medium was replaced by RPMI1640 with 10% FBS and 2 μg /ml of puromycin for selection. Following antibiotic selection, a library coverage of > 3000x was maintained for subsequent screens.

CRISPR screens

After puromycin selection, each CRISPR library replicate with ~4000x coverage was split three parts, one for mycobacterial infection, one for Shigella infection, and one used as control to verify library representation. 100 μg/ml of gentamicin was added to the cell culture to kill extracellular pathogen post infection. Survival host cells were harvested and pelleted by centrifugation with coverage of >500 cells per sgRNA. The pooled screens were performed as three independent replicates.

Genomic DNA extraction, barcode amplification, next generation sequencing and analysis

Genomic DNA (gDNA) from live cells was isolated using a homemade modified salt precipitation method as described previously (21 ). The gDNA concentrations were quantitated by Nanodrop. The sgRNA cassette was amplified and prepared for lllumina sequencing (FliSeq2000) as described previously (22). The sequencing reads were deconvoluted to generate a matrix of read counts which were then normalized under each condition by the following formula: Log2 (Reads per sgRNA/total reads per condition*10 ⁶ +1 ). The Log2 fold change of each sgRNA was determined by comparing infected sample and uninfected samples for each biological replicate. To evaluate the rank and statistical significance of genes, CRISPR screen analysis tool developed by Genetic Perturbation Platform (GPP) at the Broad institute was used. Genetic hits identified by CRISPR screens were used to perform gene-set enrichment using the g:Profiler tool (23). The KEGG, Reactome and the Gene Ontology (Biological Process) were used as the pathway databases to identify gene sets. Enrichment Map was used for interpretation of the biological processes (24).

Validation of individual sgRNAs

For each sgRNA cloning, spacer-encoding sense and antisense oligonucleotides with BsmBI-compatible overhangs were annealed, cloned into the lentiGuide-Puro vector, and verified by sequencing (Table 9). Lentivirus was generated in HEK293FT cells using Lipofectamine 2000 and PLUS reagents following manufacturer’s instructions. Lentiviral transduction was performed in dCas9-KRAB-expressing THP-1 cells to generate individual knockdown THP-1 cells. After 11 days of puromycin selection, each knockdown THP-1 cell was infected by M. bovis BCG or S. flexneri to validate their phenotypes, such as host cell survival and intracellular pathogen growth, as top genetic hits identified by CRISPR screens.

RNA extraction and RT-qPCR analysis

Total RNA was extracted using the RNeasy Plus kit (Qiagen) according to the manufacturer’s instructions. cDNA synthesis was performed with iScript cDNA synthesis kit (Bio-Rad). qPCR was performed using iTaq Universal SYBR Green Supermix (Bio-Rad) with a 20 μL reaction consisting of 5 ng of cDNA, 10 μL of supermix, and 5 μM of primers. The qPCR reactions were run on CFX Connect Real-Time PCR Detection System (Bio-Rad). Relative quantification of mRNA was performed using GAPDH mRNA as internal control. Primers are summarized in Table 10.

Table 10 Primers used in Surveyor assay and RT-qPCR analysis.

Western blotting

THP-1 cells were lysed in M-PER mammalian protein extraction reagent (Thermo Scientific) supplemented with Pierce protease inhibitor (Thermo Scientific) followed by shaking at 4°C for 10 minutes. Protein concentration was measured by Pierce BCA protein assay (Thermo Scientific). Protein samples were mixed with 4x Laemmli buffer (Bio-Rad), denatured at 70°C for 10 minutes, and loaded onto ExpressPlus PAGE Gel (Genscript). Proteins were transferred onto Polyvinylidene difluoride (PVDF) membrane using iBIot blotting system (Invitrogen). After blocking with 5% non-fat milk solution in Phosphate Buffered Saline with Tween 20 (PBST), membrane was incubated with following primary antibodies: anti-CRISPR- Cas9 monoclonal antibody 7A9-3A3 (Abeam, 1 :1 ,000) and Vinculin monoclonal antibody (Enzo, 1 :20,000). Secondary incubation was performed with goat anti-mouse IgG HRP conjugate (Thermo Scientific, 1 :10,000). Protein bands were visualized with Pierce ECL Western Blotting Substrate (Thermo Scientific) by autoradiography.

Surveyor assay

Genomic DNA was extracted from cell cultures using the DNeasy Blood and Tissue kit (QIAGEN). AAVSI target locus was amplified by PCR with the high fidelity KOD-Plus-Neo DNA polymerase (TOYOBO) using primers listed in Table 10. 200-400 ng of the PCR amplicons were denatured, reannealed, and incubated with 1 μL of Surveyor Nuclease S and 1 μL of Enhancer S (IDT) at 42oC for 1 h. After incubation, 6 μL of digested product was loaded onto a polyacrylamide gel (4-12%) and run at 120 V for 2.5 h. Gel was stained with ethidium bromide and imaged with a Gel Doc imaging system (Bio-Rad). Quantification was based on band intensity. The ratios of the uncleaved to cleaved DNA bands were used to calculate the percentage of insertion-deletion mutations (indel) in the starting cell population.

Cytokine quantification

Supernatants were collected at indicated times post bacterial infection. Cytokine and chemokine levels in mycobacterial-infected supernatants were determined using human cytokine and chemokine and growth factor 45-plex panel 1 (Thermo Scientific) according to the manufacturer’s instructions. Cytokine and chemokine levels in Shigella- infected supernatants were determined using Bio-plex pro human cytokine 17-plex and IFN-a2 kit (Bio- Rad) according to the manufacturer’s instructions. The results were measured by a Bio-Plex 200 system (Bio-Rad). Oxylipin Analysis

Oxylipins extraction and liquid chromatography-mass spectrometry (LC-MS) analysis followed the published reports with modifications (25). Briefly, cell cultures were harvested at given time points, rapidly quenched and spun down. Cell pellets were resuspended in 1 mL acetonitrile/methanol (1:1), containing 10 μL antioxidant solution (0.2 mg/ml_ BHT/EDTA) and 10 μL of internal standard solution, and lysed mechanically with 0.1 -mm silica beads by using Qiagen Tissuelyser II. The lysates were collected and evaporated to dryness in a vacuum evaporator, and the dry extracts were redissolved in 100 μL of acetonitrile/methanol (1 :1 ) for LC-MS analysis.

The LC-MS/MS analysis was performed with Agilent 1290 Ultra Pressure Liquid Chromatography (UPLC, Waldbronn, Germany) coupled to an electrospray ionization with Funnel Technology on a triple Quadrupole mass spectrometer. Chromatographic separation was achieved using Acquity UPLC BEH C18 (2.0 x 150 mm, 1.7 pm; Milford, MA, USA) with a flow rate of 0.30 mL/min at 40°C during a 35 min gradients. (0-3.5 min from 15% B to 33% B, 3.5-5.5 min B to 38%, 5.5-7 min to 42% B, 7-9 min to 48% B, 9-16 min to 60% B, 16-20 min to 75 % B, 20-24 min to 85 % B, 24-25 min to 100% B which was held for 2 min, then returned to initial condition over 0.1 min), while using the solvents A, 0.1 % acetic acid, and B, 90:10 v/v acetonitrile/isopropanol. The auto-sampler was cooled at 4°C and 5 μL of the extract was injected. Electrospray ionization was performed in negative ion mode with the following source parameters: drying gas (N2) temperature 200°C with a flow of 14 L/min, nebulizer gas pressure 30 psi, sheath gas temperature 400°C with a flow of 11 L/min, capillary voltage 3,000 V and nozzle voltage 800 V. Data acquisition and processing were performed using MassHunter software (Agilent Technologies, US).

Metabolite profiling

Metabolite extraction and targeted metabolomics analyses followed the published reports with modifications (26). Briefly, cell cultures were harvested at given time points and rapidly quenched, and metabolites were extracted using acetonitrile:methanol:water (2:2:1). After centrifugation, the supernatant was collected and evaporated to dryness in a vacuum evaporator, and the dry extracts were redissolved in 100 μL of 98:2 water/methanol for liquid chromatography-mass spectrometry (LC-MS) analysis.

The targeted LC-MS/MS analysis was performed with Agilent 1290 ultrahigh pressure liquid chromatography system coupled to a 6490 Triple Quadrupole mass spectrometer equipped with a dual-spray electrospray ionization source with Jet StreamTM (Agilent Technologies, Santa Clara, CA). Chromatographic separation of metabolites in central carbon metabolism was achieved by using Phenomenex (Torrance, CA) RezexTM ROA-Organic Acid H+ (8%) column (2.1 c100 mm, 3 μm) and the compounds were eluted at 40°C with an isocratic flow rate of 0.3 ml. min ¹ of 0.1% formic acid in water. Compounds were quantified in multiple reaction monitoring (MRM) mode. Electrospray ionization was performed in both positive and negative ion modes with the following source parameters: drying gas temperature 300°C with a flow of 10 L min ^-1 , nebulizer gas pressure 40 psi, sheath gas temperature 350°C with a flow of 11 L min ^-1 , nozzle voltage 500 V, and capillary voltage 4,000 V and 3,000 V for positive and negative mode, respectively. Data acquisition and processing were performed using MassHunter software (Agilent Technologies, US), and cell counts were normalized to correct variations in sample preparation.

Flow cytometry

Cas9 activity was tested by transducing pXPR_011 (Addgene #59702) which expresses GFP and sgRNA against GFP into Cas9-expressing THP-1 cells. The extent of GFP loss was quantitated by flow cytometry. Ten days post infection, cells were washed and resuspended in 1 xPBS supplemented with 2% FBS. After passing through EASYstrainer cell sieves (VWR) to remove any clumps of cells, samples were measured by LSRII Fortessa flow cytometer (Becton Dickinson). At least 30,000 cells were recorded per sample. BD FACS Aria II Cell Sorter was used to collect dCas9-KRAB-expressing THP-1 cells which express both BFP and dCas9-KRAB proteins. 110,000 BFP positive cells were collected. Forward and side scatter were used to identify appropriate THP-1 cell populations.

Imaging

To visualize intracellular GFP-reporter M. bovis BCG (map24:: GFP) and RFP-reporter S. flexneri M90T AvirG (uhpT.·. dsRed), infected THP-1 cells were directly observed under a confocal fluorescence microscope (Zeiss LSM 700).

Example 1 : Establish host-pathogen model systems suitable for high-throughput genetic screens.

To maximize host-pathogen interactions for subsequent genetic screening, the present disclosure first established the robust and stable mycobacterial and Shigella infections of human monocytic leukemia THP-1 cells, which have been extensively used to study monocyte/macrophage functions. Various experimental conditions were tested, such as multiplicity of infection (MOI) and infection times in mycobacterial infection. The phagocytosis by THP-1 cells of macrophage-activated fluorescent Mycobacterium bovis BCG (map24::GFP) were also assessed. As shown in Figure 1A, this strain produced green fluorescent protein after phagocytosis. To examine the effect of increasing the MOI on mycobacterial infection, bacterial cells were added to the THP-1 cell culture at MOI ranging from 3:1 to 50:1 . Only some of the host cells were infected at 72 hours (Figure 1 A). To improve the efficiency of mycobacterial infection, M. bovis BCG cells were added multiple times (0, 18, 42 hours) at an MOI of 10:1 to the THP-1 cell culture. After 72 hours, more than 95% of THP- 1 cells were infected by M. bovis BCG, suggesting that three rounds of infection substantially improved infection efficiency (Figure 1A). Besides, THP-1 cells were stained by trypan blue and counted to assess the cell viability after mycobacterial infection. The number of THP-1 cells significantly decreased at 72 hours after three rounds of bacterial infection (Figure 1 E), compared with the single round of bacterial infection at various MOIs (Figures 1 B-D), indicating that intracellular M. bovis BCG induced host cell death. In addition, the present disclosure assessed the phagocytosis by THP-1 cells of macrophage-activated fluorescent S. flexneri M90T AvirG (uhpT. :dsRed). Considering that S. flexneri can rapidly trigger host cell death, infection with S. flexneri during a short period of time (0.5 to 5 hours) was tested. As shown in Figure 2A, S. flexneri produced red fluorescent protein after THP-1 phagocytosis, and 3 hours incubation was enough for the bacterial strain to infect host cells. Moreover, the number of THP-1 cells significantly decreased 24 hours post-infection, after either the initial 3 or 5 hours infection (Figures 2D and E), compared with 0.5 and 1.5 hours infection (Figures 2B and C). The above results indicate that intracellular M. bovis BCG and S. flexneri induce host cell death, which can be used as an indicator for subsequent high-throughput genetic screening.

Example 2: Selection for genetic perturbations by genome-wide CRISPR knockout and CRISPRi screening systems in human THP-1 cells.

To enable the genome-wide CRISPR knockout and CRISPRi screens, monoclonal Cas9 and dCas9-KRAB THP-1 cell lines were first constructed. These cell lines were then transduced with lentiviruses encoding CRISPR knockout and CRISPRi sgRNA libraries (Figure 3). Those pooled THP-1 screen libraries were infected with M. bovis BCG or S. flexneri M90T. Survival host cells were then harvested and processed for next generation sequencing. To further validate genetic hits identified by genome-wide screens, customized secondary CRISPR knockout and CRISPRi screen libraries were designed and prepared for screening. As shown in Figure 4, THP-1 cells that constitutively express Cas9 or dCas9-KRAB protein were constructed and validated. Following blasticidin or FACS-based selection, heterogeneous polyclonal pools of Cas9- and dCas9-KRAB-expressing THP-1 cells were generated. To obtain THP-1 cells that retain stable Cas9 and dCas9-KRAB expression, monoclonal cells were isolated by limiting dilution. Expression of Cas9 and dCas9 protein in monoclonal THP-1 cells was evaluated by Western blot (Figure 4A and B), and the total protein content of THP-1 cells was quantified by BCA protein assay. No expression of Cas9 or dCas9 protein was detected in wild-type THP-1 cells. In contrast, several monoclonal Cas9 and dCas9-KRAB expressing THP-1 cells expressed Cas9 and dCas9 proteins, suggesting that Cas9 and dCas9-KRAB expressing THP-1 cells have been constructed. To determine the knockout function of Cas9 in THP-1 cells, sgRNA targeting enhanced green fluorescent protein (EGFP) were tested. In the Cas9-expressing THP-1 cell line, EGFP fluorescence was abolished in 91% of cells after 11 days of puromycin selection, suggesting that the EGFP sequence was cleaved by Cas9 (Figure 4C). Moreover, transduction of Cas9 expressing THP- 1 cells with lentivirus expressing AAVS1 -targeting sgRNA indicated genomic cleavage at AAVS1 locus (Figure 4D). Transduction of dCas9-KRAB expressing THP-1 cells with lentivirus expressing sgRNAs targeting genes known to strongly affect cell proliferation, such as INTS9 and MCM2, significantly inhibited the growth of THP-1 cells (Figure 4E). Transcripts of INTS9 and MCM2 were inhibited in dCas9-KRAB expressing THP-1 cells with sgRNA targeting INTS9 and MCM2, respectively (Figure 4F and G). Those results demonstrated that Cas9 and dCas9-KRAB expressing THP-1 cell lines were constructed for subsequent genome-wide screen library preparation. The Brunello CRISPR knockout library contains 76,441 sgRNAs that systematically target 19,114 distinct human genes with 1 ,000 non-targeting controls. The Dolcetto CRISPRi library contains 57,050 sgRNAs that target 18,901 distinct human genes with 500 non-targeting controls. Three separate lentiviral CRISPR knockout and CRISPRi libraries were prepared. To find optimal virus volumes to achieve a multiplicity of infection (MOI) of 0.3, which ensures that most transduced cells receive only one genetic perturbation, lentiviral titer was determined through transduction. To assess the effective representation of the genome-wide CRISPR knockout and CRISPRi libraries in the present disclosure, sgRNA barcodes from THP-1 cells without bacterial infection were sequenced. As shown in Figures 5 a-c and g-i, the majority of our sgRNAs were detected in the libraries (>99.999%), with high perfectly matching guides (> 80%) and high uniformity across constructs (8.9 skew ratio of top 10% to bottom 10%). Distribution of the sgRNAs cross control and bacteria-infected samples showed that the sgRNA distribution in M. bovis BCG- and S. flexneri- infected conditions were skewed compared to the baseline conditions in both CRISPR knockout and CRISPRi screens, with some sgRNAs enriched and others depleted (Figures. 5 d-f and j-l).

In order to identify top genetic hits in mycobacterial infection, a false discovery rate (FDR) < 0.1 and Log2-fold change > 1 was used as cut-off and observed positive selection of 141 and 157 genes in CRISPR knockout and CRISPRi screens, respectively, with 48 genes enriched in both screens (p-value of overlap <2.087E-66; Figure 6, Tables 1 and 2). Biological pathway analysis was next performed to identify enriched biological processes in mycobacterial infection. Both CRISPR knockout and CRISPRi screens identified the same pathways, such as interferon signaling, DNA repair, chromatin organization, cellular stress response, the p53 pathway, and signaling by receptor tyrosine kinases (Figures 6 A and B, and Table 3). Top genetic hits are associated with important immune response pathways in host cells, such as type I interferon (IFN) signaling pathways (IFNAR1 , IFNAR2, TYK2, IRF9, STAT2, and JAK1), and genes involved in antimicrobial peptides production (FIDAC2 and CREBBP). Moreover, the CRISPRi screen identified specific pathways in mycobacterial infection, such as TCA cycle, mitochondrial biogenesis, and lipid metabolism pathways, which partially supports the capability of the CRISPRi screen to identify essential genes of the host cells (Table 3). Top genetic hits include the YPEL5 gene, which is involved in phagosome function; the WDR26 gene, which is involved in apoptosis, ubiquitination, signal transduction, and other cellular processes; UCHL5, together with UBA7 and UBE2L6, may regulate the conjugation and secretion of ISG15, which plays an essential role in antimycobacterial immunity; and the AHR/ARNT genes, which are involved in lipid metabolism in mycobacterial infection.

Table 1 Genetic hits from the genome-wide CRISPR-Cas9 knockout screen in M. bovis BCG infection

Table 2 Genetic hits from the genome-wide CRISPRi screen in M. bovis BCG infection

Table 3. Enrichment of biological pathways identified by genome-wide CRISPR KO (a) and CRISPRi (b) screens in mycobacterial infection.

Similarly, in order to identify top genetic hits in Shigella infection, a false discovery rate (FDR) < 0.25 and Log2-fold change > 1 was used as cut-off and positive selection of 76 and 28 genes were observed in CRISPR knockout and CRISPRi screens, respectively, with 10 genes enriched in both screens (p-value of overlap <7.394E-18; Figure 6, Table 4 and 5). Next, the present disclosure performed biological pathway analysis to identify enriched biological processes in Shigella infection. Both CRISPR knockout and CRISPRi screens identified the same pathways, such as Toll-like receptors cascades, chromatin organization, pyruvate metabolism, the cellular stress response pathway, and receptor tyrosine kinase signaling (Figures 6 C and D, and Table 6). Top genetic hits are associated with chromatin modifying pathways (MSL2, KMT2D, KMT2C, and KDM4A), Toll-like receptor signaling (TRAF6, TIFA, IRAKI and MYD88), pyruvate metabolism (PDHB, DLAT, PDHA1 , MPC1 , MPC2, and CS) and genes involved in antimicrobial peptides production pathways (HDAC2, and CREBBP). Moreover, CRISPRi screens identified essential gene targets in Shigella infection, such as genes involved in PIP3/AKT signaling pathways (LAMTOR3, MAP2K7, MAPK9 and LAMTOR2), and chromatin modifying pathways (EP400, SAP18, and DPY30).

Table 4 Genetic hits from the genome-wide CRISPR-Cas9 knockout screen in S. flexneri infection

Table 5 Genetic hits from the genome-wide CRISPRi screen in S. flexneri infection

Table 6. Enrichment of biological pathways identified by genome-wide CRISPR KO (a) and

CRISPRi (b) screens in Shigella infection.

Example 3: Design and construct secondary CRISPR knockout and CRISPRi screen libraries to validate genome-wide genetic hits.

Customized secondary CRISPR knockout and CRISPRi screen libraries were constructed based on following reasons: to test potential host targets identified in literatures, but not have scored in the genome-wide screens and to validate genome-wide screen hits in mycobacterial and Shigella infection. Both secondary CRISPR knockout and CRISPRi libraries contain 4, 740 sgRNAs that target 372 human genes with 10 sgRNAs per gene. As shown in Figure 7 A and Table 7, 25 and 26 genes in CRISPR knockout and CRISPRi screens were identified with 8 genes enriched in both screens in mycobacterial infection (FDR < 0.05 and Log2 FC > 0.5). A validation rate of genes were next calculated in the secondary screen based on FDR threshold of <5% and grouped by their p-value in the primary screens (Figure 7B). Validation rates of genes in secondary screens deceased with the decreasing of p-value in primary genome-wide CRISPR knockout and CRISPRi screens, indicating that genome- wide screen, with lower number of sgRNA per gene, in mycobacterial infection was robust and reliable. Moreover, top genetic hits identified by primary genome-wide screens were also scored by secondary CRISPR knockout and CRISPRi screens, such as TYK2, IFNAR2, IFNAR1 , STAT2, JAK1 and IRF9 (Figure 7C). As shown in Figure 7D, genes identified in mycobacterial infection by both genome-wide and secondary CRISPR screens were summarized. In Shigella infection, 23 and 29 genes in secondary CRISPR knockout and CRISPRi screens were identified with 12 genes enriched in both screens (FDR < 0.05 and Log2 FC > 0.5) (Figure 8A and Table 8). Similarly, the greater of primary screen p-value, the greater of validation rate in secondary screen in Shigella infection (Figure 8B). Furthermore, the average fold change of top genetic hits in both genome-wide and secondary CRISPR screens indicate the reliability and robustness of genome-wide screens (Figure 8C). Screen hits associated with diverse signaling pathways, which identified by both genome-wide and secondary screens, were summarized in Figure 8D.

Table 7A Genetic hits from 2 ^nd CRISPR-Cas9 knockout screen in M. bovis BCG infection

Table 7B Genetic hits from 2 ^nd CRISPRi screen in M. bovis BCG infection

Table 8A Genetic hits from 2 ^nd CRISPR-Cas9 knockout screen in S. flexneri infection

Table 8B Genetic hits from 2 ^nd CRISPRi screen in S. flexneri infection

Example 4: Validation of host targets and drug inhibitors in mycobacterial infection.

To further verify the function of top genetic hits, individual gene knockdown THP-1 cells were constructed and their phenotype in mycobacterial infection (Table 9) were confirmed. After 3 rounds of M. bovis BCG infection, host cell viability and intracellular mycobacterial level were measured over time (Figures 9A and B). The correlation between screen phenotype and validation phenotype suggested that repression of screen hits indeed enhanced host cell survival with 88.9% true positive rate (Figure 9A). In addition, repression of screen hits inhibited intracellular M. bovis BCG growth (Figure 9B). Since many top genetic hits are key components of type I IFN signaling pathway which regulates cytokine and chemokine production in human immune cells, to characterize the function of those top hits in mycobacterial infection, cytokine and chemokine production in IFNAR1 and TYK2 knockdown THP-1 cells (Figure 9C) were profiled. As shown in Figures 10A and B, M. bovis BCG infection significantly stimulated the production of many cytokines and chemokines, such as IL-1 RA, IL-1β, IL-6, IL10, IL-18, CXCL10, CCL4, and CCL5. Intriguingly, knockdown transcripts of IFNAR1 and TYK2 genes abolished infection-induced cytokine and chemokine production, such as IFNAR1 and TYK2 gene knockdown in THP-1 cells abolished the production of infection-induced cytokines and chemokines, including IL-1 Ra, which inhibits the pro- inflammatory function of IL-1α and IL-1β by binding to their receptor, as well as CXCL10 and CCL4, important biomarkers for monitoring TB treatment and disease progression (Figure 10C). The present disclosure subsequently tested the function of specific small molecules that are thought to target some of the top hits identified herein. For instance, Cerdulatinib, used for B-cell malignancies treatment in phase 2a clinical trial, is a selective inhibitor of TYK2 (IC50=0.5 nM) and JAK1 (IC50=12 nM). CH223191 is a selective inhibitor of AHR. Host cells were treated with drug inhibitors 1 hour before M. bovis BCG infection. Host cell survivability and intracellular M. bovis BCG level were measured post infection. In line with TYK2 gene knockdown phenotype, Cerdulatinib enhanced host cell survival and inhibited intracellular mycobacterial growth in a dose-dependent manner (Figures 9D and E). Besides, 100 nM of Cerdulatinib abolished infection-induced cytokine and chemokine production, such as IL-1 RA, CXCL10, CCL4, and IL-10 (Figure 9F). As shown in Figures 9G and H, AHR inhibitor, CH223191 , enhanced host cell survival and controlled intracellular mycobacterial growth in a dose-dependent manner. Post-infection treatment with cerdulatinib and CH223191 gave similar results (Figure 9I).

To determine the mechanism underlying the protection of host cells by AHR inhibitor, oxylipins production which is regulated by AHR-activated cytochrome P450 production (Figure 11 A) was profiled. As expected, knockdown of AHR gene expression in THP-1 cells decreased CYP-regulated oxylipin production post-infection (Figure 11 B). In THP-1 cells without CRISPRi knockdown, CH223191 abrogated infection-induced and CYP-regulated oxylipin production (Figures 11 D and E and N). M. bovis BCG infection induced LXA4 (lipoxin A4) production but inhibited the production of PGE2 (prostaglandin E2), a compound critical for blocking Mtb replication and protecting host cells (Figures 11 F and G). Strikingly, CH223191 (3 μM) abolished infection-induced LXA4 production, stimulated PGE2 production, and restored the PGE2/LXA4 ratio to pre-infection levels without affecting arachidonic acid production (Figures 11 C and F-H). CH223191 also induced the production of non-CYP- regulated oxylipins 5-HETE, 5-OxoETE, 11 -, 12-, 15-HETE, and linoleic acid (Figures 11 l-M). Those data comprehensively demonstrated AHFt inhibitor, CH223191 , protects host cell and controls mycobacterial growth by regulating oxylipins metabolism.

The screens with M. bovis BCG identified several host targets potentially involved in Mtb infection, but these targets required validation with Mtb infection models to establish their clinical relevance. To test the function of TYK2 and AHFt inhibitors in Mtb infection, THP-1 cell- Mtb and primary human macrophage-Mtb infection models were infected with Mtb H37Rv-lux, then added inhibitors and measured host cell and pathogen survival (Figure 12A).

Consistent with results in M. bovis BCG infection, both cerdulatinib and CH223191 reduced Mtb growth in THP-1 cells in a dose-dependent fashion (Figures 12B and 12C). Whereas CH223191 was effective against both mycobacterial species at identical concentrations, higher concentrations of cerdulatinib were needed to restrict Mtb growth than to restrict M. bovis BCG growth. The drugs were effective in combination, though adding 0.3 μM of CH223191 did not significantly increase the efficacy of 1 μM cerdulatinib (Figure 12D). Imatinib, an Mtb HDT, also restricted Mtb growth in this system (Figures 12B and 12C).

In primary human macrophages, cerdulatinib, CH223191 , and their combination each significantly reduced host cell death by Mtb as measured by lactate dehydrogenase (LDH) release; these inhibitors outperformed imatinib (Figure 12E). The experimental compounds also restricted Mtb growth as measured by bacterial luminescence (Figure 12F). Bacterial load was also assessed: cerdulatinib and CH223191 reduced Mtb CFUs (Figure 12G). Thus, inhibiting the type I IFN signaling pathway or the AHR/ARNT pathway restricts the mycobacterial burden and preserves host cell survival during M. bovis BCG and Mtb infection of human monocytic phagocytes, a major cell type infected by Mtb.

Example 5: Validation of host targets and drug inhibitors in Shigella infection.

To verify the function of top positive genetic hits, THP-1 cells were next constructed with individual gene knockdowns and their phenotypes were confirmed in S. fiexneri infection (Table 9). The positive correlation between screen phenotype and validation phenotype confirmed that repression of positive screen hits indeed enhanced host cell survival with a 92.3% true positive rate (Pearson R=0.56; Figure 13A). Moreover, repression of the transcription of MYD88, TRAF6, and IRAKI , key components in the TLR1/2 signaling pathway, also inhibited intracellular S. fiexneri growth (Figure 13B).

To characterize how the inhibition of those positive genetic hits mediates the host cell response and provides protection, cytokine and chemokine production regulated by the TLR1/2 signaling pathway (Figure 13C) was measured. Knockdown of the transcription of either MYD88 or IRAKI abolished the production of infection-induced pro-inflammatory cytokines and chemokines, such as IL-1β, IL-2, and IL-8 (Figures 13D and 14A). As a potential strategy to control intracellular bacterial infection by targeting host factors, the function of corresponding small molecule inhibitors was tested. IRAKI /4-lnh, a selective inhibitor of IRAKI , inhibited pro-inflammatory cytokine and chemokine production and protected host cells in a dose-dependent manner (Figures 13E and 14B-D). Compared to similar levels of intracellular S. flexneri after 2 hours of infection, the number of intracellular pathogens decreased in the presence of IRAKI inhibitor 24 hours post-infection, indicating that inhibition occurred by controlling intracellular pathogen growth rather than by blocking pathogen entry (Figure 13F). Ginsenoside Compound K (CK), a metabolite of Panax ginseng that also inhibits IRAKI , similarly enhanced host cell survival, inhibited S. flexneri growth, and abolished infection-induced IL-8 production in THP-1 cells (Figure 15).

In addition to dysregulating the host immune response, S. flexneri grows rapidly and replicates in host cells but does so only if there is an adequate supply of nutrients. Knockout or knockdown of components of the pyruvate dehydrogenase complex or the pyruvate transporter MPC1/2 in the mitochondria redirected central metabolism, favoring the survival of THP-1 cells infected with S. flexneri (Figures 8D and 16A), which is congruent with the induction by S. flexneri in epithelial cells of the production of acetyl-CoA (27). The present disclosure next tested the function of the PDFIB inhibitor oxythiamine (OT), as well as its combination with an IRAKI inhibitor (IRAKI /4-lnh), in S. flexneri infection. OT treatment enhanced host cell survival post-infection (Figure 16B) but failed to control intracellular S. flexneri growth (Figure 16C), which is consistent with the PDFIB gene knockdown phenotype (Figures 13A and B). Interestingly, the combination of both IRAKI and PDFIB inhibitors (IRAKI /4-lnh and OT) significantly enhanced host cell survival and controlled S. flexneri growth better than treatment with either of these inhibitors alone, indicating a synergistic effect of inhibitors targeting both immune and non-immune pathways in macrophages (Figures 16B and C). Furthermore, in a PMA-stimulated THP-1 - S. flexneri infection model, IRAK1/4-lnh, OT, and their combination enhanced host cell survival and limited intracellular pathogen growth (Figures 16D-F).

In line with a previous study of HeLa cells (27), the present disclosure found that S. flexneri induced acetyl-CoA production in THP-1 cells, suggesting that, in both cases, S. flexneri supports its own rapid intracellular growth and replication by manipulating the central metabolism of the host cell (Figure 16G). Moreover, 0.1 mM of PDFIB inhibitor decreased infection-induced acetyl-CoA and downstream succinate production, which shifts host metabolism and leads to enhanced host cell survival (Figures 16G and FI). The combination of both IRAKI and PDFIB inhibitors reduced acetyl-CoA and succinate production to the uninfected levels, thus limiting intracellular S. flexneri growth and propagation (Figures 16G and H).

To further validate the function of inhibitors, the present disclosure tested host cell death and intracellular S. flexneri growth in a primary human macrophage infection model (Figure 17A). Consistent with the results in the THP-1 infection model, IRAK1/4-lnh, OT, and their combination each significantly reduced host cell death by intracellular S. flexneri as measured by lactate dehydrogenase (LDFI) release (Figure 17B). Those small molecule inhibitors also restricted intracellular S. flexneri growth as measured by counting bacterial colony forming units (CFUs) (Figure 17C). Thus, inhibiting the TLR 1/2 signaling pathway or the pyruvate catabolism signaling pathway restricts the intracellular pathogen burden and preserves host cell survival during S. flexneri infection of human macrophages.

Table 9 Genetic hits and inhibitors validated in M. bovis BCG and S. flexneri infections

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