The p53 is the master regulator of the cell known for regulating a large array of cellular processes. Inactivation of p53 by missense mutations is one of the leading causes of cancer. Some of these mutations endow p53 with selective oncogenic functions to promote tumor progression. Due to the vast array of mutations found in p53, the experimental studies showing the role of different mutant p53 as an oncogene are also expanding. In this review, we discuss the oncogenic roles of different p53 mutants at the cellular level identified by multi-omics tools. We discuss some of the therapeutic studies to tackle p53 mutants and their downstream targets identified by omics. We also highlight the future prospective and scope of further studies of downstream p53 targets by omics.

The amino acid hypusine (Nε-4-amino-2-hydroxybutyl(lysine)) occurs only in isoforms of eukaryotic translation factor 5A (eIF5A) and has a role in initiating protein translation. Hypusinated eIF5A promotes translation and modulates mitochondrial function and oxygen consumption rates. The hypusination of eIF5A involves two enzymes, deoxyhypusine synthase and deoxyhypusine hydroxylase (DOHH). DOHH is the second enzyme that completes the synthesis of hypusine and the maturation of eIF5A. Our current study aims to identify inhibitors against DOHH from Leishmania donovani (LdDOHH), an intracellular protozoan parasite causing Leishmaniasis in humans. The LdDOHH protein was produced heterologously in Escherichia coli BL21(DE3) cells and characterized biochemically. The three-dimensional structure was predicted, and the compounds folic acid, scutellarin and homoarbutin were selected as top hits in virtual screening. These compounds were observed to bind in the active site of LdDOHH stabilizing the structure by making hydrogen bonds in the active site, as observed by the docking and molecular dynamics simulation studies. These results pave the path for further investigation of these molecules for their anti-leishmanial activities.

Preeclampsia is a pregnancy-specific disease that can have serious effects on the health of both mothers and their offspring. Predicting which women will develop preeclampsia in early pregnancy with high accuracy will allow for improved management. The clinical symptoms of preeclampsia are well recognized, however, the precise molecular mechanisms leading to the disorder are poorly understood. This is compounded by the heterogeneous nature of preeclampsia onset, timing and severity. Indeed a multitude of poorly defined causes including genetic components implicates etiologic factors, such as immune maladaptation, placental ischemia and increased oxidative stress. Large datasets generated by microarray and next-generation sequencing have enabled the comprehensive study of preeclampsia at the molecular level. However, computational approaches to simultaneously analyze the preeclampsia transcriptomic and network data and identify clinically relevant information are currently limited. In this paper, we proposed a control theory method to identify potential preeclampsia-associated genes based on both transcriptomic and network data. First, we built a preeclampsia gene regulatory network and analyzed its controllability. We then defined two types of critical preeclampsia-associated genes that play important roles in the constructed preeclampsia-specific network. Benchmarking against differential expression, betweenness centrality and hub analysis we demonstrated that the proposed method may offer novel insights compared with other standard approaches. Next, we investigated subtype specific genes for early and late onset preeclampsia. This control theory approach could contribute to a further understanding of the molecular mechanisms contributing to preeclampsia.

Identification of cancer-related genes is helpful for understanding the pathogenesis of cancer, developing targeted drugs and creating new diagnostic and therapeutic methods. Considering the complexity of the biological laboratory methods, many network-based methods have been proposed to identify cancer-related genes at the global perspective with the increasing availability of high-throughput data. Some studies have focused on the tissue-specific cancer networks. However, cancers from different tissues may share common features, and those methods may ignore the differences and similarities across cancers during the establishment of modeling. In this work, in order to make full use of global information of the network, we first establish the pan-cancer network via differential network algorithm, which not only contains heterogeneous data across multiple cancer types but also contains heterogeneous data between tumor samples and normal samples. Second, the node representation vectors are learned by network embedding. In contrast to ranking analysis-based methods, with the help of integrative network analysis, we transform the cancer-related gene identification problem into a binary classification problem. The final results are obtained via ensemble classification. We further applied these methods to the most commonly used gene expression data involving six tissue-specific cancer types. As a result, an integrative pan-cancer network and several biologically meaningful results were obtained. As examples, nine genes were ultimately identified as potential pan-cancer-related genes. Most of these genes have been reported in published studies, thus showing our method's potential for application in identifying driver gene candidates for further biological experimental verification.

Recent advances in direct cell reprogramming have made possible the conversion of one cell type to another cell type, offering a potential cell-based treatment to many major diseases. Despite much attention, substantial roadblocks remain including the inefficiency in the proportion of reprogrammed cells of current experiments, and the requirement of a significant amount of time and resources. To this end, several computational algorithms have been developed with the goal of guiding the hypotheses to be experimentally validated. These approaches can be broadly categorized into two main types: transcription factor identification methods which aim to identify candidate transcription factors for a desired cell conversion, and transcription factor perturbation methods which aim to simulate the effect of a transcription factor perturbation on a cell state. The transcription factor perturbation methods can be broken down into Boolean networks, dynamical systems and regression models. We summarize the contributions and limitations of each method and discuss the innovation that single cell technologies are bringing to these approaches and we provide a perspective on the future direction of this field.

Mycobacterium tuberculosis (MTB), the causative agent of tuberculosis (TB), encodes a family of membrane proteins belonging to Resistance-Nodulation-Cell Division (RND) permeases also called multidrug resistance pumps. Mycobacterial membrane protein Large (MmpL) transporters represent a subclass of RND transporters known to participate in exporting of lipid components across the cell envelope. These proteins perform an essential role in MTB survival; however, there are no data regarding mutations in MmpL, polyketide synthase (PKS) and acyl-CoA dehydrogenase FadE proteins from Khyber Pakhtunkhwa, Pakistan. This study aimed to screen mutations in transmembrane transporter proteins including MmpL, PKS and Fad through whole-genome sequencing (WGS) in local isolates of Khyber Pakhtunkhwa province, Pakistan. Fourteen samples were collected from TB patients and drug susceptibility testing was performed. However, only three samples were completely sequenced. Moreover, 209 whole-genome sequences of the same geography were also retrieved from NCBI GenBank to analyze the diversity of mutations in MmpL, PKS and Fad proteins. Among the 212 WGS (Accession ID: PRJNA629298, PRJNA629388, and ERR2510337-ERR2510345, ERR2510546-ERR2510645), numerous mutations in Fad (n = 756), PKS (n = 479), and MmpL (n = 306) have been detected. Some novel mutations were also detected in MmpL, PKS and acyl-CoA dehydrogenase Fad. Novel mutations including Asn576Ser in MmpL8, Val943Gly in MmpL9 and Asn145Asp have been detected in MmpL3. The presence of a large number of mutations in the MTB membrane may have functional consequences on proteins. However, further experimental studies are needed to elucidate the variants' effect on MmpL, PKS and FadE functions.