HLA-A, HSPA5, IGFBP5 and PSMA2 Are Restriction Factors for Zika Virus Growth in Astrocytic Cells

(1) Background: Zika virus (ZIKV), an arbo-flavivirus, is transmitted via Aeges aegyptii mosquitoes Following its major outbreaks in 2013, 2014 and 2016, WHO declared it a Public Health Emergency of International Concern. Symptoms of ZIKV infection include acute fever, conjunctivitis, headache, muscle & joint pain and malaise. Cases of its transmission also have been reported via perinatal, sexual and transfusion transmission. ZIKV pathologies include meningo-encephalitis and myelitis in the central nervous system (CNS) and Guillain-Barré syndrome and acute transient polyneuritis in the peripheral nervous system (PNS). Drugs like azithromycin have been tested as inhibitors of ZIKV infection but no vaccines or treatments are currently available. Astrocytes are the most abundant cells in the CNS and among the first cells in CNS infected by ZIKV; (2) Methods: We previously used SOMAScan proteomics to study ZIKV-infected astrocytic cells. Here, we use mass spectrometric analyses to further explain dysregulations in the cellular expression profile of glioblastoma astrocytoma U251 cells. We also knocked down (KD) some of the U251 cellular proteins using siRNAs and observed the impact on ZIKV replication and infectivity; (3) Results & Conclusions: The top ZIKV dysregulated cellular networks were antimicrobial response, cell death, and energy production while top dysregulated functions were antigen presentation, viral replication and cytopathic impact. Th1 and interferon signaling pathways were among the top dysregulated canonical pathways. siRNA-mediated KD of HLA-A, IGFBP5, PSMA2 and HSPA5 increased ZIKV titers and protein synthesis, indicating they are ZIKV restriction factors. ZIKV infection also restored HLA-A expression in HLA-A KD cells by 48 h post-infection, suggesting interactions between this gene product and ZIKV.


Introduction
Zika virus (ZIKV) belongs to the family Flaviviridae and contains a 11 kb long positive single stranded (ss) RNA genome encoding 3 structural and 7 non-structural proteins surrounded by a nucleocapsid, made from viral capsid protein, enveloped in a host-derived lipid bilayer [1,2]. First isolated from non-human primates in 1947, ZIKV outbreaks date back to 2013 [2,3]. 2016 marked the peak number of cases in the US according to the CDC [4]. WHO reported 80 countries with cases of ZIKV transmission as of Feb 2022 [5]. ZIKV transmission is primarily by Aedes aegyptii mosquitoes [6]. ZIKV also can be transmitted in utero or by sexual and transfusion methods [5,[7][8][9][10]. However, these epidemiological results are far from accurate because 80% of infections are asymptomatic or non-specific, lack of sufficient routine surveillance, and cross-reactivity with other flaviviruses like Dengue virus (DENV) [4,5,7]. Some of the symptoms associated with ZIKV infection include acute fever, conjunctivitis, muscle and joint pain, headache and arthralgia [11]. ZIKV pathology includes a multitude of neurological conditions such as Guillain-Barré Syndrome (GBS) and beads were washed 2× with 70% ethanol then by 100% ACN. After the washing steps, trypsin was added at an enzyme to protein ratio of 1:25 and the proteins were digested into peptides, eluted and labelled with the TMT 6-plex system. Labelled samples were analyzed by liquid chromatography/tandem mass spectrometry (LC -MS/MS).

siRNA-Mediated Knock Down
For initial screens, 20 µM stock solutions of lyophilized siRNA were prepared by dissolving them in 1× siRNA buffer. 5000 U251 cells in each well of a 96-well plate were treated with 80 nM of each of various SMARTPool On-Target plus siRNA targeting a variety of cellular genes. Scrambled control non-silencing siRNA were used for the negative control treatment. Dharmafect/OptiMEM mixture was used for the transfection. After siRNA treatment, cells were incubated at 37 • C for 48 h, after which their cell viability was measured by WST-1 assay (described below). At 48 h post siRNA treatment (hpt), U251 cells were infected with ZIKV at an MOI of 3 (described above) and the supernatants were collected for viral titer measurements using plaque assays. All the experiments were performed in triplicates.
To knockdown specific genes, 20 µM stock solutions of lyophilized siRNA were prepared by dissolving them in 1× siRNA buffer. Knockdown of HLA-A, HSPA5, IGFBP5 and PSMA2 was performed using the SMART-Pool siRNAs for each at 25 nM. Scrambled siRNA was used as negative control. U251 cells were plated in 6-well plates, grown to 40% confluency and siRNA, solubilized in OptiMEM/Dharmfect, were added. Cells were treated with siRNAs for 48 h. In some cases, a second treatment was performed 24 h after the first siRNA treatment. Afterwards, ZIKV infections were done at an MOI of 3 and supernatants and cell pellets were collected for viral plaque assay and viral protein synthesis at 48 hpi. The experiments were performed in triplicates.

WST-1 Assay
Cell viability was determined using the WST-1 assay in 96-well plate format. Cells in each well were treated with 8 µL of WST-1 reagent after 48-72 h of siRNA treatment and incubated at 37 • C for 1.5 h. Absorbances at 440 nm and 610 nm were recorded using a plate reader and 610 nm absorbance values of infected samples were subtracted from their corresponding 440 nm absorbance values before being normalized to their respective mock samples. An average of 4-6 replicates per condition was calculated to determine cell viability values.

Statistical and Bioinformatic Analyses
Numerous peptide sequences were identified by mass spectrometric analyses and proteins were identified and quantified from at least 2 different non-redundant peptides in its sequence. This resulted in expectation values and calculated false discovery values of 0.1%, using the xTandem (https://www.thegpm.org, accessed on 28 October 2022) peptide identification software. Protein quantity fold changes between infected and each timematched mock samples were converted to log 2 values and significance determined by Students t-test and by Z-score analyses as described [54].  (Table 1). Only 50 U251 cellular proteins were significantly over-expressed or under-expressed at a F.C. cut-off of +/-2.5 ( Table 2). 33 proteins were under-expressed while 17 were over-expressed in ZIKV-infected U251 cells compared to their time-matched mock-infected U251 samples. Examples of ZIKV-mediated under-expressed proteins include STC1 (−14.47-fold), IGFBP5 (−6.90-fold), and MDK (−5.02-fold) at 48 hpi ( Table 2). Examples of ZIKV-mediated overexpressed proteins include OAS2 (3.87-fold), HLA-B (4.20-fold), and CXCL11 (8.61-fold) at 48 hpi ( Table 2). The most over-expressed protein was CXCL11 (8.61-fold) and the most under-expressed protein was STC1 (−14.47-fold) ( Table 2). We applied F.C. cut off values of >1.3 and <0.750 to provide sufficient stringency to the data while keeping the number of dysregulated targets high enough for subsequent bioinformatic analyses.

Numerous U251 Cellular Networks and Proteins Are Dysregulated by ZIKV at 48 hpi
The largest number of significantly dysregulated proteins and cellular networks were dysregulated by ZIKV at 48 hpi and these pertain to viral replication, antiviral responses, interferon signaling, and antimicrobial responses ( Figure 1). Some of the proteins involved in each of these top dysregulated cellular networks include IFNG, IFNIL1, IRF5, PARP9 and STAT2 (Figure 1). The top dysregulated cellular network at 24 hpi was cell death and survival, connective tissue development and function and energy production, while the top-most dysregulated network at 48 hpi was antimicrobial response, cell signaling and infectious diseases (Figure 2A Figure  2A). At 24 hpi, some of the over-expressed proteins include MBD4, DMXL2, MX2 and LRRC17 while some of the under-expressed include SERF2, WNT2B and LSM6 ( Figure  2B). This shows ways the virus hijacks cellular machinery that consequently results in the dysregulation of important astrocytic functions. The top dysregulated cellular network at 24 hpi was cell death and survival, connective tissue development and function and energy production, while the top-most dysregulated network at 48 hpi was antimicrobial response, cell signaling and infectious diseases (Figure 2A   , Top dysregulated cellular network in ZIKV-infected U251 cells at 24 hpi is Cell Death and survival, connective tissue development and function and energy production network. Overlaid 12 and 48 hpi data shown in smaller diagrams. Green represents U251 proteins that were significantly under-expressed compared to their time-matched mock-infected values while red represents proteins that were significantly over-expressed compared to their time-matched mock-infected samples. 2-tailed Students t-test and Z-score analyses were used to determine the statistical significance.

Numerous Cellular Functions and Canonical Pathways Are Activated or Inhibited at 48 hpi
Since the largest number of significantly dysregulated proteins were at 48 hpi, top dysregulated cellular functions are depicted in Figure 3. Predicted activation was identified for cellular functions such as cytotoxicity of cells, progressive neuromuscular disease, antigen presentation and neuromuscular disease, while predicted inhibition was identified for cellular functions namely replication of virus and viral life cycle ( Figure 3). For example, over-expressed proteins such as HLA-A, HLA-B and HLA-E and under-expressed proteins such as LGALS3 and SORT1 contribute to cytotoxicity of cells ( Figure 3). Some of the U251 activated canonical pathways at 48 hpi include interferon signaling, role of pattern recognition receptors in recognition of bacteria and viruses, role of PKR in interferon induction and antiviral response and Th1 pathway, while some of the IPA predicted inhibited canonical pathways include PD-1, PD-L1 cancer immunotherapy pathway and coronavirus pathogenesis pathway ( Figure 4).

Numerous Cellular Functions and Canonical Pathways Are Activated or Inhibited at 48 hpi
Since the largest number of significantly dysregulated proteins were at 48 hpi, top dysregulated cellular functions are depicted in Figure 3. Predicted activation was identified for cellular functions such as cytotoxicity of cells, progressive neuromuscular disease, antigen presentation and neuromuscular disease, while predicted inhibition was identified for cellular functions namely replication of virus and viral life cycle (Figure 3). For example, over-expressed proteins such as HLA-A, HLA-B and HLA-E and under-expressed proteins such as LGALS3 and SORT1 contribute to cytotoxicity of cells ( Figure 3). Some of the U251 activated canonical pathways at 48 hpi include interferon signaling, role of pattern recognition receptors in recognition of bacteria and viruses, role of PKR in interferon induction and antiviral response and Th1 pathway, while some of the IPA predicted inhibited canonical pathways include PD-1, PD-L1 cancer immunotherapy pathway and coronavirus pathogenesis pathway ( Figure 4). Predicted dysregulation of host cell diseases and functions at 48 hpi. Cut off p-value is 0.05. Z-score threshold is +/− 1.96σ. Green represents U251 proteins that were significantly underexpressed by ZIKV infection compared to their time-matched mock-infected samples, while red represents proteins that were significantly over-expressed by infection compared to their timematched mock-infected samples. 2-tailed Students t-test and Z-score analyses were used to determine the statistical significance. Orange represents predicted activation of the cellular function and disease, and blue represents prediction inhibition as generated by IPA software based on the statistically significant data from the comparison of ZIKV-infected U251 cells to time-matched mockinfected U251 cells at 48 hpi. Predicted dysregulation of host cell diseases and functions at 48 hpi. Cut off p-value is 0.05. Z-score threshold is +/− 1.96σ. Green represents U251 proteins that were significantly under-expressed by ZIKV infection compared to their time-matched mock-infected samples, while red represents proteins that were significantly over-expressed by infection compared to their timematched mock-infected samples. 2-tailed Students t-test and Z-score analyses were used to determine the statistical significance. Orange represents predicted activation of the cellular function and disease, and blue represents prediction inhibition as generated by IPA software based on the statistically significant data from the comparison of ZIKV-infected U251 cells to time-matched mock-infected U251 cells at 48 hpi. Students T-test was used to determine statistical significance (top axis), and degree of predicted activation/inhibition was determined by Z-score (bottom axis). For each of them, the cut off p-value is 0.05 and the cut off Z-score value is +/− 1.96σ.

Knocking Down U251 Genes Affects ZIKV Growth
To ascertain the effects of disrupting proteins that were either over-expressed or under-expressed by ZIKV infection, we selected 50 cellular genes and knocked them down by siRNA treatment. Cell viability of non-infected cells after KD was >90% ( Figure 5A). Cell viability of most infected KD cells also were not significantly reduced, except for IGFBP2, HLA-B, IGFBP7 and MBD4, which resulted in approximately 40% reduction in viability ( Figure 5A). KD of most proteins resulted in a 2-fold to 10-fold increase in viral titers while others decreased the viral titers by 1.5 to 2-fold ( Figure 5B). For example, APO-BEC3D, GNS, GRN, HLA-A and IGFBP5 siRNA treatments increased ZIKV titers at both timepoints ( Figure 5B). APOBEC3D siRNA caused 7-and 3-fold increase, HLA-A siRNA caused 8-and 4-fold increase, and IGFBP5 caused 3-and 1.5-fold increase at 48 hpi and 72 hpi compared to NSC-treated U251 cells ( Figure 5B). Other siRNAs, such as MBD4, decreased viral titers at either time point by roughly 2-fold. TIMP2 KD reduced viral titers by roughly 1.4-fold. Students T-test was used to determine statistical significance (top axis), and degree of predicted activation/inhibition was determined by Z-score (bottom axis). For each of them, the cut off p-value is 0.05 and the cut off Z-score value is +/− 1.96σ.

Knocking Down U251 Genes Affects ZIKV Growth
To ascertain the effects of disrupting proteins that were either over-expressed or underexpressed by ZIKV infection, we selected 50 cellular genes and knocked them down by siRNA treatment. Cell viability of non-infected cells after KD was >90% ( Figure 5A). Cell viability of most infected KD cells also were not significantly reduced, except for IGFBP2, HLA-B, IGFBP7 and MBD4, which resulted in approximately 40% reduction in viability ( Figure 5A). KD of most proteins resulted in a 2-fold to 10-fold increase in viral titers while others decreased the viral titers by 1.5 to 2-fold ( Figure 5B). For example, APOBEC3D, GNS, GRN, HLA-A and IGFBP5 siRNA treatments increased ZIKV titers at both timepoints ( Figure 5B). APOBEC3D siRNA caused 7-and 3-fold increase, HLA-A siRNA caused 8-and 4-fold increase, and IGFBP5 caused 3-and 1.5-fold increase at 48 hpi and 72 hpi compared to NSC-treated U251 cells ( Figure 5B). Other siRNAs, such as MBD4, decreased viral titers at either time point by roughly 2-fold. TIMP2 KD reduced viral titers by roughly 1.4-fold. . Cell viabilities and ZIKV yields are compared to those in U251 cells treated with non-silencing scrambled control (NSC). Error bars show standard deviation. * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, **** p-value < 0.0001.

HSPA5, PSMA2, IGFBP5 and HLA-A KD Result in Increased ZIKV NS1 Expression and Titers
KDs of several genes were initially optimized. Cell viabilities did not decrease by day 4 post-treatment after 25 nM and 50 nM siRNA treatment with APOBEC3D and HLA-A siRNAs ( Figure 6). HLA-A KD increased cell viability by about 140% (to 240%) compared to non-treated control (NTC) by day 4 ( Figure 6A). Both 25 nM and 50 nM IGFBP5 siRNA treatments decreased cell viability to about 85% by day 2 and increased it back to 110%

HSPA5, PSMA2, IGFBP5 and HLA-A KD Result in Increased ZIKV NS1 Expression and Titers
KDs of several genes were initially optimized. Cell viabilities did not decrease by day 4 post-treatment after 25 nM and 50 nM siRNA treatment with APOBEC3D and HLA-A siRNAs ( Figure 6). HLA-A KD increased cell viability by about 140% (to 240%) compared to non-treated control (NTC) by day 4 ( Figure 6A). Both 25 nM and 50 nM IGFBP5 siRNA treatments decreased cell viability to about 85% by day 2 and increased it back to 110% and 130% by day 4 ( Figure 6B). Cell viability results for PSMA2 and HSPA5 siRNA treatments are shown in Figure 6D-F. PSMA2 siRNA treatment decreased the cell viability to 0.6-fold of non-treated by day 4, while HSPA5 siRNA treatment decreased it to 0.7 fold by day 3 and increased it to 1.2 fold by day 4 (Figure 6C,D). treatments are shown in Figure 6D-F. PSMA2 siRNA treatment decreased the cell viability to 0.6-fold of non-treated by day 4, while HSPA5 siRNA treatment decreased it to 0.7 fold by day 3 and increased it to 1.2 fold by day 4 ( Figure 6C,D). A single 50 nM treatment with HSPA5 and PSMA2 siRNA was more effective at knocking down HSPA5 and PSMA2 protein expression in U251 cells 2 and 4 days posttreatment, while 25 nM single treatment of PSMA2 and HSPA5 was more effective in knocking down by day 4 ( Figure 7A). For HLA-A, single treatment of 50 nM siRNA successfully knocked down HLA-A expression by day 2 and day 4 post siRNA treatment ( Figure 7B). Both 80 nM and 100 nM concentrations of IGFBP5 siRNA knocked down the protein expression by days 2 and 4 post single treatment successfully ( Figure 7C).        More NS1 was expressed in non-treated U251 cells compared to scrambled siRNAtreated U251 cells ( Figure 9E). ZIKV infection restored HLA-A expression in HLA-A KD U251 cells by 48 hpi (Figure 9A). HSPA5 KD, PSMA2 KD and IGFBP5 KD remained stable More NS1 was expressed in non-treated U251 cells compared to scrambled siRNAtreated U251 cells ( Figure 9E). ZIKV infection restored HLA-A expression in HLA-A KD U251 cells by 48 hpi (Figure 9A). HSPA5 KD, PSMA2 KD and IGFBP5 KD remained stable throughout the course of infection ( Figure 9B-D). Western blot quantification of the siRNA KD proteins and ZIKV NS1 in each condition is shown in Figure 9F-J, respectively. HLA-A KD treatment decreased HLA-A expression to 0.2-fold that of the non-silencing siRNAtreated U251 cells ( Figure 9F). ZIKV restored the expression of HLA-A in HLA-A KD conditions by 2.5 fold by 48 hpi (Figure 9F). ZIKV NS1 expression increased 2-fold in the presence of the HLA-A KD by 48 hpi (Figure 9F). HSPA5 KD decreased HSPA5 expression by 5-fold and the KD remained stable in the presence of ZIKV infection ( Figure 9G). HSPA5 KD increased ZIKV NS1 expression by 1.8-fold ( Figure 9G). PSMA2 KD decreased PSMA2 expression 5-fold and it remained stable in ZIKV infected conditions by 48 hpi (Figure 9H). PSMA2 KD did not impact ZIKV NS1 expression in U251 cells by 48 hpi (Figure 9H). IGFBP5 siRNA decreased IGFBP5 expression 8-fold in U251 cells and it remained stable in ZIKV-infected conditions by 48 hpi (Figure 9I). ZIKV infection alone was also able to reduce IGFBP5 expression in U251 cells at 48 hpi ( Figure 9I). IGFBP5 siRNA treatment increased ZIKV NS1 expression 2-fold compared to that of non-silencing control siRNA treatment, despite not being statistically significant ( Figure 9I). Finally, ZIKV NS1 expression was 0.6-fold in the non-silencing siRNA treated U251 cells compared to that in non-treated U251 cells by 48 hpi (Figure 9J).

ZIKV Infection Causes Restoration of HLA-A Levels in HLA-A KD Cells
HLA-A KD increased ZIKV NS1 expression over time with 24 hpi being the first time point when any NS1 expression was detected ( Figure 10A). HLA-A expression was partially restored in HLA-A KD U251 cells after 24 hpi and the restoration of HLA-A expression increased by 48 hpi ( Figure 10A). HSPA5 KD increased ZIKV NS1 expression over time with the first expression being observed at 24 hpi ( Figure 10B). HSPA5 KD remained unaffected by ZIKV infection ( Figure 10B). Western blot quantification of siRNA-mediated KD proteins and ZIKV NS1 in each of the KD conditions at 3, 6, 12, 24, 36, and 48 hpi in comparison to non-silencing control are shown in Figure 10C,D, respectively. HLA-A siRNA treatment decreased HLA-A expression by 9-fold compared to that in scrambled siRNA-treated U251 cells by 3 hpi, irrespective of the presence of ZIKV infection ( Figure 10C). Over time, the HLA-A-siRNA mediated KD remained stable ( Figure 10C). ZIKV restored the expression of HLA-A and increased it by 6-fold in comparison to the HLA-A KD mock-infected U251 cells by 48 hpi (Figure 10C). HLA-A KD increased ZIKV NS1 expression more than scrambled siRNA ( Figure 10C). HLA-A KD in U251 cells increased ZIKV NS1 expression by 1.8-fold compared to that in scrambled siRNA-treated U251 cells by 48 hpi (Figure 10C). Earlier time points also showed increased ZIKV NS1 expression in HLA-A KD U251 cells than in time-matched scrambled siRNA-treated ZIKV-infected U251 cells despite not being statistically significant ( Figure 10C). HSPA5 KD decreased HSPA5 expression by 8-fold compared to the scrambled siRNA treatment by 3 hpi, irrespective of the presence of ZIKV infection ( Figure 10D). Over time, HSPA5 KD remained stable in U251 cells although the levels of HSPA5 in scrambled siRNA-treated U251 cells also slightly decreased over time ( Figure 10D). HSPA5 KD in U251 cells was unaffected by ZIKV infection ( Figure 10D). HSPA5 KD increased ZIKV NS1 expression in U251 cells more than the scrambled siRNA ( Figure 10D). Western quantification showed HSPA5 KD increasing ZIKV NS1 expression by 1.5-fold than that in ZIKV-infected scrambled siRNA-treated U251 cells by 48 hpi (Figure 10D). Earlier time points showed higher ZIKV NS1 expression in HSPA5 KD U251 cells than in the time-matched scrambled siRNA-treated ZIKV-infected U251 cells despite not being statistically significant ( Figure 10D).  (A,B), respectively. All error bars are standard deviations and 2-tailed Students t-test was used to measure statistical significance and calculate p-values. * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, **** p-value < 0.0001. ns means not significant.

Discussion
We used a mass spectrometry-based proteomic approach to understand ZIKV infection in U251 cells, followed by siRNA-mediated knockdown studies to delineate the importance of different host cellular proteins in ZIKV replication and protein synthesis. Most of the proteomic pathway analyses were performed using IPA software to generate top ZIKV dysregulated cellular networks, functions and canonical pathways and they helped explain some of the ZIKV-mediated molecular dysregulations that could potentially lead  (A,B), respectively. All error bars are standard deviations and 2-tailed Students t-test was used to measure statistical significance and calculate p-values. * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001, **** p-value < 0.0001. ns means not significant.

Discussion
We used a mass spectrometry-based proteomic approach to understand ZIKV infection in U251 cells, followed by siRNA-mediated knockdown studies to delineate the importance of different host cellular proteins in ZIKV replication and protein synthesis. Most of the proteomic pathway analyses were performed using IPA software to generate top ZIKV dysregulated cellular networks, functions and canonical pathways and they helped explain some of the ZIKV-mediated molecular dysregulations that could potentially lead to broader clinical manifestations. The fold-change cut-off was chosen to be >1.333 and <0.750 to increase the stringency, while keeping the number of statistically significant proteins large enough for meaningful bioinformatic analyses (Table 1). For example, OAS3, which negatively regulates interferon/chemokine responsive genes and modulates innate response against Chikungunya infection [24,56,57], was over-expressed (Figure 2A), while SERP1, which regulates stress responses and interacts with viral NS4B protein to suppress replication in cases of DENV type 2 infections [58,59], was under-expressed ( Figure 2A). Similarly, MBD4, which has a role in DNA methylation, repair and CNS development [41,60,61], and MX2, which is a restriction factor for other viruses including HIV, were over-expressed ( Figure 2B), while WNT2B, which regulates cell growth, adult CNS development and differentiation and innate responses via the CTNNB1 signaling pathway [62][63][64][65][66], was under-expressed ( Figure 2B).
Among the ZIKV dysregulated cellular functions at 48 h post ZIKV infection, which was the timepoint with the largest number of dysregulated proteins, MAPK1 was predicted to be activated and STAT1/2 inhibited (Figure 1). MAPK1 dysregulation induces chorioretinal atrophy and optic nerve abnormalities in ZIKV infections and STAT1/2 is involved in antiviral responses; in addition, ZIKV NS5 protein-mediated STAT2 degradation modulates type I and III interferon responses [51,[67][68][69]. Other molecules dysregulated by ZIKV among the top ZIKV dysregulated functions at 48 hpi included over-expressed proteins such as STAT1, IFIT2/3 and HLA-A and under-expressed proteins such as LAMP2 (Figure 3). Many of them have crucial roles in healthy cellular functions and are also involved in other viral infections. STAT1 mediates antiviral type I, II and III interferon responses, regulates ZIKV-mediated induction of cholesterol 25-hydroxylase and interacts with STAT2 for its ZIKV infection regulation via ZIKV NS2A interaction [68,[70][71][72][73]. IFIT2/3 regulate apoptotic processes, stabilize IFIT1 and promote its binding to viral RNA for translation inhibition [65, 74,75]. HLA-A has a role in WNV and DENV infections and disease severity in HIV and SARS-CoV2 infections [76][77][78][79][80]. LAMP2 regulates lysosome biogenesis and autophagosome maturation in other viral infections such as African swine fever virus (ASFV) by interacting with ASFV E248R and E199L proteins and DENV infections [54,[81][82][83][84]. Therefore, their roles in ZIKV infection need to be further examined to help better understand ZIKV modulation of astrocytic functions to aid its infection and replication.
Finally, several ZIKV-dysregulated canonical pathways including the Th1 pathway, role of pattern recognition receptors and interferon signaling highlight the role of ZIKV in neuroinflammation and CNS immune modulation as astrocytes are known to be involved in CNS tissue repair, inflammation, NF-kB pathway and MAPK pathways ( Figure 4) [85,86]. Moreover, cognitive functions, synaptic plasticity and DNA and RNA viral load control via Type I interferon receptor (IFNAR) signaling are among the other astrocytic functions potentially dysregulated by ZIKV [85,87,88]. We had previously used the SOMAScan platform and identified ZIKV-induced U251 proteomic dysregulations [54]. Here, we combined the results from both studies, identified 50 proteins dysregulated at least 2.5-fold, either upwards or downwards, and ascertained what effects KD of each of these genes would have ( Figure 6). Proteins like APOBEC3D and HLA-A not only increased ZIKV titers upon being KD but also cell viability ( Figure 5). From the list of siRNA targets, APOBEC3D, HLA-A and IGFBP5 siRNA KD were among the few that increased ZIKV titers while MBD4, TIMP and TNC KD decreased them (Figure 7). APOBEC3D is a cytidine deaminase and inhibits other viruses such as HIV-1 and human cytomegaloviruses [89][90][91]. Unfortunately, the APOBEC3D antibody we tested did not work, so it was not followed up with. HLA-A class I & II have roles in WNV and secondary DENV infections, and in HIV and SARS-CoV2 disease severity [76][77][78][79][80]. IGFBP5 interacts with heparan sulfate proteoglycans (HSGs) and cell-surface matrix glycoproteins, which are receptors for HIV tat, HSV 1&2 and DENV [92]. PSMA2 and HSPA5 were included because we have found they are also involved in viral replication. PSMA2 is crucial for 20S proteosome complex assembly, degradation of damaged proteins and is involved in influenza-mediated escape from viral clearance via inhibition of NRF2-regulated oxidative stress response while HSPA5 facilitates binding, entry and protein folding for many viruses including Ebola, BDV, MERS-CoV, SARS-CoV2, DENV E and HBV [93][94][95][96][97]. After determining the concentration of siRNA that resulted in a successful KD by 48 hpi, with at least 60% cell viability, U251 cells were KD using siRNAs targeting PSMA2, HSPA5, HLA-A or IGFBP5 (Figures 6 and 7). This resulted in an increase in ZIKV titers, highlighting their role as potential restriction factors in U251 cells (Figure 8). HLA-A and IGFBP5 had contrasting results. ZIKV increased HLA-A expression and reduced IGFBP5 expression but KD of each increased ZIKV titers (Figure 8). This highlights their differing roles in ZIKV replication. IGFBP5, an insulin like growth factor binding protein, is an IGF signaling regulator and is crucial for cell proliferation, growth and survival. Therefore, ZIKV decreasing its expression modulates growth and leads towards increased cell death as we reported previously [54]. In addition, IGFBP5 is an activator of PI3K/AKT and MAPK pathways which in turn are utilized by other viruses such as Ebola virus for cell entry [98]. Hence, later upon its KD, it could increase ZIKV efficiency to establish infection and replicate faster in U251 cells. However, HLA-A is a major histocompatibility complex antigen, ubiquitously expressed in nearly all nucleated cells with its role in endogenous peptide presentation to CD8+ T cells. Thus, increase in HLA-A in the presence of ZIKV could be a host response to viral infection and KD of it circumvents this response allowing faster replication ( Figure 8). This highlights how KD of genes, either up-or down-regulated by the virus, can still exert differing effects on viral titers depending on the function and thus further studies are warranted.
Interestingly, ZIKV also restored HLA-A expression over time in HLA-A KD cells, highlighting potential cross-talk between HLA-A and ZIKV proteins (Figures 9 and 10). This is interesting because HLA-A expression in astrocytes is mostly in cells confined to CNS lesions [99,100]. Moreover, HLA-A also has roles in antigen presenting cells (APC). Therefore, it will be interesting to look at how HLA-A directs ZIKV proteins, if at all, to the surface for antigen presentation [101,102]. HLA-A alleles are also associated with Guillain-Barré syndrome (GBS) in different populations [103][104][105]. GBS is a rare neurological disorder in which the body's immune system attacks part of its peripheral nervous system [106]. In 2020, the HLA-A33 allele was found in a SARS-CoV2 induced GBS patient [107]. HLA-A also is associated with acute inflammatory demyelinating polyradiculoneuropathy (AIDP) [108]. Since limited research on HLA-A's function in nervous system pathologies exist, contrasting associations have been shown between HLA-A and GBS [109,110]. In Iraqi patients with GBS, decreased HLA-A:0101 frequency was found in 2016 while in 2014, in GBS patients from East Coast of Australia, HLA ligands were found to be more prevalent [109,110]. Interactions between HLA-A and killer immunoglobulin-like receptors (KIRs) have been associated with GBS and Multiple Sclerosis (MS) patients as either risk or protective factors [109]. In 1998, HLA types were found to be associated with GBS onset in Japanese patients [111]. HLA-A role has also been shown to be important in Schwann cells that act as facultative APCs in peripheral nervous system and increase HLA Class I expression during GBS [112]. Therefore, additional work is warranted to understand the role of HLA-A in ZIKV infections. Finally, HSPA5 KD also increased ZIKV NS1 expression ( Figure 10). Since HSPA5 interacts with the ZIKV envelope, regulated unfolded protein response, and alters the ER environment [83,[113][114][115], its role in flaviviral infection also needs to be further examined.
Numerous proteomic and transcriptomic studies have been conducted to explain ZIKV infection in microcephalic fetuses, primary human fetal neural progenitor cells, serum samples, placental tissues, and astrocyte-derived cell lines [45][46][47]49]. This study identified numerous cellular proteins, some of which were similar to previous studies while others were novel. Among the similarities, fibronectin was found to be downregulated by ZIKV in U251 cells in this study. Our previous SOMAScan proteomic study also implicated ZIKV-induced damage to placental integrity [45,54]. Cytokines and chemokines such as IL-6 were identified as downregulated while IL-8 and CCL5 were upregulated in this study ( Table 2) and in our previous study [54]. They were also found to be upregulated in ZIKVinfected human brain cortical astrocytes [116]. RNA-seq studies done in mouse primary astrocytes revealed common functions such as neuron development, brain development and neuromuscular diseases to be dysregulated by ZIKV [53], similar to the current study ( Figure 3). Insulin like growth factor responses were also implicated in ZIKV infection by Shereen et al. [53], highlighting the potential role of IGFBP5 in ZIKV infection. Therefore, further studies to explore it are warranted. EDNRB also was one of the genes that was identified as down-regulated at both the mRNA and protein levels, respectively [53] and at the protein level in the current study (Table 2). An orthogonal study in 2018 on ZIKV infection in human neural progenitor neuronal cell line SK-N-BE2 also identified markers involved in similar cellular functions and processes as were identified in this study, including cell growth, cell cycle, cell death, NS development and function, and neurological diseases (Figures 2 and 3) [117]. In addition, molecules involved in PI3K/AKT and ERK/MAPK pathways were found to be implicated in ZIKV infection in both this study (Figures 2 and 3) and the orthogonal study [54,117]. In 2021, a TMT 10-plex system approach followed by LC-MS/MS on 12 placental samples from 2016 in Puerto Rico revealed cellcell signaling and neurological disease as among the top dysregulated pathways and functions [46], similar to some of the findings in our study (Figures 2 and 3). In addition to similarities, there are a few differences between this study and the previously published omics studies. For example, we used human glioblastoma astrocytoma U251 cells as  [46,53,117]. Furthermore, Shereen et al. performed an RNA-seq study while we complemented the LC-MS/MS proteomic study with genetic KD [53]. Among the novelties in the findings of the current study, antigen presentation was predicted to be upregulated by ZIKV infection in U251 cells at the protein level ( Figure 3) [49]. This is consistent with the increase in HLA-A expression observed in ZIKV-infected U251 cells and the consequent increase in ZIKV replication and NS1 protein synthesis post HLA-A KD conditions (Table 2; Figure 10). Energy production dysregulation was a cellular function differentially identified in this study unlike LC-MS/MS studies done previously on primary neural progenitor cells and human placental samples in 2018 and 2021 [46,49]. Another difference between this study and the orthogonal study done by Scaturro et al. is that they focused on ZIKV host protein-protein interactions and phosphoproteomic profiling via affinity purification integrated LC-MS/MS (AP-LC-MS/MS) and on kinase substrate relations/regulatory sites through PhosphitePlus51 resource while this study looked at proteomic impact of ZIKV infection via LC-MS/MS.

Conclusions
In conclusion, ZIKV dysregulates U251 cellular networks, functions and canonical pathways at the cell-wide level, with the impact being more severe at later time points than at earlier time points. The siRNA-mediated KD revealed that proteins like APOBEC3D, HLA-A and IGFBP5 KD increase ZIKV titers while TIMP2, MBD4 and TNC KD decrease ZIKV titers by either 48 or 72 hpi. Further analyses of ZIKV NS1 and ZIKV titers revealed that PSMA2, HSPA5, HLA-A and IGFBP5 behave as restriction factors for ZIKV replication in U251 cells by 48 hpi. Finally, ZIKV restores the expression of HLA-A in HLA-A KD U251 cells by 48 hpi; therefore, further experiments need to be conducted to better understand ZIKV infection. Moreover, the use of potential pharmaceutical compounds like MG132, a PSMA2 inhibitor known to exert anti-viral activity against HSV-1, trematinib which increases HLA-A expression via IFN gamma/STAT1 signaling and STAT3 activation and other tyrosine kinase inhibitors, need to be employed to identify novel anti-viral compounds against ZIKV infection [118][119][120][121]. The similarities and novelties of the current study in comparison to previously published omics studies help us understand some of the findings from this study better and further experiments need to be conducted to explain some of these differences and similarities across different cell types and omics platforms.