Analyzing host-viral interactome of SARS-CoV-2 for identifying vulnerable host proteins during COVID-19 pathogenesis

Abstract

The development of therapeutic targets for COVID-19 treatment relies on understanding the molecular mechanism of pathogenesis. Identifying genes and proteins involved in the infection mechanism is the key to shedding light on the complex molecular mechanisms. The combined effort of many laboratories distributed throughout the world has produced protein and genetic interactions. We integrated available results and obtained a host protein-protein interaction network composed of 1432 human proteins. Next, we performed network centrality analysis to identify critical proteins in the derived network. Finally, we performed a functional enrichment analysis of central proteins. We observed that the identified proteins are primarily associated with several crucial pathways, including cellular process, signaling transduction, neurodegenerative diseases. We focused on the proteins that are involved in human respiratory tract diseases. We highlighted many potential therapeutic targets, including RBX1, HSPA5, ITCH, RAB7A, RAB5A, RAB8A, PSMC5, CAPZB, CANX, IGF2R, HSPA1A, which are central and also associated with multiple diseases.

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

“…Previous COVID-19 studies reported that hub-high traffic genes in the brown module, such as RBX1 ( 207 – 210 ), PSMA3 ( 211 ), PSMA6 ( 212 ), PTEN ( 213 ), VPS29 ( 214 , 215 ), SNRPE ( 216 ), PSMA4 ( 217 ), TXN ( 218 ), PTGES3 ( 219 , 220 ), RHOA ( 57 , 221 , 222 ), CANX ( 209 ), B2M ( 223 ), GDI2 ( 57 , 224 , 225 ), TXNL1 ( 226 ), SUMO1 ( 147 , 227 , 228 ), PSMC2 ( 229 ), REEP5 ( 230 ), DECR1 ( 108 ), RAB10 ( 224 ), PRDX3 ( 231 , 232 ), ACTR2 ( 215 ), TNFSF13B ( 192 , 233 , 234 ), and ARPC3 ( 215 ), have a central role in host-SARS-COV-2 interactions. For example, ARDS and ALI in patients with severe COVID-19 were directly associated with increased expression of the PTEN and RHOA hub-high traffic genes, respectively ( 213 , 222 ).…”

“…Additionally, some of the purple module hub-high traffic genes, such as MAPK1 ( 189 , 374 ), CUL2 ( 210 ), CMTM6 ( 162 ), TXNRD1 ( 375 ), RAB1A ( 376 ), DICER1 ( 377 ), RAB5A ( 209 ), HSP90B1 ( 343 ), MAGT1 ( 378 ), ADAM10 ( 379 , 380 ), SNX2 ( 89 ), OLA1 ( 381 ), SPTLC1 ( 382 ), SH3GLB1 ( 383 ), TIMM10B ( 384 ), and CREB1 ( 385 ) hub-high traffic TF, which are central for information exchange in this module, are potential targets for development of COVID-19 therapeutic strategies. Among these, the mitogen-activated protein kinase 1 ( MAPK1 ) hub-high traffic gene is a potential core target for many anti-COVID-19 therapeutic strategies ( 189 , 374 , 386 – 390 ).…”

“…Interestingly, the ras-related protein Rab-5A ( RAB5A ) is involved in various autophagy processes such as autophagosomal maturation and early autolysosome formation, and the SARS-CoV-2 enhances autophagy by increasing expression of this gene to accelerate viral replication ( 391 ). Furthermore, RAB5A is a key host protein for interaction with SARS-CoV-2 and may be an important target for inhibiting COVID-19 progression ( 209 ).…”

Exploiting the molecular basis of age and gender differences in outcomes of SARS-CoV-2 infections

“…At the end of 2019 in Wuhan (China), medical facilities reported acute pneumonia cases with an unknown origin. Further analysis revealed that a novel coronavirus, named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was responsible for that disease, subsequently called coronavirus disease 2019 (COVID-19) [11] , [20] . The clinical manifestations spanned from asymptomatic infection to severe pneumonia and a severe state of inflammation (molecularly characterised by a cytokine storm) leading to a fatal outcome [40] , [87] , [67] , [6] , [60] , [38] .…”

Beyond COVID-19 Pandemic: Topology-aware optimisation of vaccination strategy for minimising virus spreading

“…Despite this, we retain that modeling the spreading using a classical SEIR (Susceptible-Exposed-Infective-Recovered) model may not be the best choice since some pa-rameters are considered at a global scale, while the spreading involves single contacts. In parallel, some previous works such as [17,18,19,20,21,22] have demonstrated that the use of a model coming from graph theory may be helpful to describe the spreading. In this way, comprehensive graphs may be derived using nodes, i.e., people, and edges, i.e., their contacts.…”