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Fulltext Molecular characterization and genetic diversity of four undescribed novel oleaginous Mortierella alpina strains from Libya

Muftah Eltariki FE, Tiwari K, Alhoot MA

F1000Res, 2021;10:895.
PMID: 34745563 DOI: 10.12688/f1000research.70644.1

Background: A large number of undiscovered fungal species still exist on earth, which can be useful for bioprospecting, particularly for single cell oil (SCO) production. Mortierella is one of the significant genera in this field and contains about hundred species. Moreover, M. alpina is the main single cell oil producer at commercial scale under this genus. Methods: Soil samples from four unique locations of North-East Libya were collected for the isolation of oleaginous Mortierella alpina strains by a serial dilution method. Morphological identification was carried out using light microscopy (Olympus, Japan) and genetic diversity of the isolated Mortierella alpina strains was assessed using conserved internal transcribed spacer (ITS) gene sequences available on the NCBI GenBank database for the confirmation of novelty. The nucleotide sequences reported in this study have been deposited at GenBank (accession no. MZ298831:MZ298835). The MultAlin program was used to align the sequences of closely related strains. The DNA sequences were analyzed for phylogenetic relationships by molecular evolutionary genetic analysis using MEGA X software consisting of Clustal_X v.2.1 for multiple sequence alignment. The neighbour-joining tree was constructed using the Kimura 2-parameter substitution model. Results: The present research study confirms four oleaginous fungal isolates from Libyan soil. These isolates (barcoded as MSU-101, MSU-201, MSU-401 and MSU-501) were discovered and reported for the first time from diverse soil samples of district Aljabal Al-Akhdar in North-East Libya and fall in the class: Zygomycetes; order: Mortierellales. Conclusions: Four oleaginous fungal isolates barcoded as MSU-101, MSU-201, MSU-401 and MSU-501 were identified and confirmed by morphological and molecular analysis. These fungal isolates showed highest similarity with Mortierella alpina species and can be potentialistic single cell oil producers. Thus, the present research study provides insight to the unseen fungal diversity and contributes to more comprehensive Mortierella alpina reference collections worldwide.
Structural and functional characterization of Solanum lycopersicum phosphatidylinositol 3-kinase C2 domain

Pak Dek MS, Padmanabhan P, Tiwari K, Todd JF, Paliyath G

Plant Physiol Biochem, 2020 Mar;148:180-192.
PMID: 31972387 DOI: 10.1016/j.plaphy.2020.01.014

Phosphatidylinositol 3-kinases (PI3Ks) are characterized by the presence of a C2 domain at the N-terminal end (class I, III); or at both the N-terminal and C-terminal ends (class II), sometimes including a Plextrin homology domain and/or a Ras domain. Plant PI3Ks are analogous to the class III mammalian PI3K. An N-terminal fragment (~170 aa) of the tomato PI3K regulatory domain including the C2 domain, was cloned and expressed in a bacterial system. This protein was purified to homogeneity and its physicochemical properties analyzed. The purified protein showed strong binding with monophosphorylated phosphatidylinositols, and the binding was dependent on calcium ion concentration and pH. In the overall tertiary structure of PI3K, C2 domain showed unique characteristics, having three antiparallel beta-sheets, hydrophobic regions, acidic as well as alkaline motifs, that can enable its membrane binding upon activation. To elucidate the functional significance of C2 domain, transgenic tobacco plants expressing the C2 domain of PI3K were generated. Transgenic plants showed defective pollen development and disrupted seed set. Flowers from the PI3K-C2 transgenic plants showed delayed wilting, and a decrease in ethylene production. It is likely that introduction of the PI3K-C2 segment may have interfered with the normal binding of PI3K to the membrane, delaying the onset of membrane lipid catabolism that lead to senescence.
Fulltext Cardiovascular Complications and its Impact on outcomes in COVID-19: An Original Research

Kumar P, Tiwari K, Pendyala SK, Jaiswal RK, Chacko NL, Srivastava E, et al.

J Pharm Bioallied Sci, 2021 Nov;13(Suppl 2):S1333-S1337.
PMID: 35017983 DOI: 10.4103/jpbs.jpbs_143_21

Introduction: The viral infection COVID-19 is highly infectious and has claimed many lives till date and is still continuing to consume lives. In the COVID-19, along with pulmonary symptoms, cardiovascular (CV) events were also recorded that have known to significantly contribute to the mortality. In our study, we designed and validated a new risk score that can predict CV events, and also evaluated the effect of these complications on the prognosis in COVID-19 patients.
Materials and Methods: A retrospective, multicenter, observational study was done among 1000 laboratory-confirmed COVID-19 patients between June 2020 and December 2020. All the data of the clinical and laboratory parameters were collected. Patients were randomly divided into two groups for testing and validating the hypothesis. The identification of the independent risk factors was done by the logistic regression analysis method.
Results: Of all the types of the clinical and laboratory parameters, ten "independent risk factors" were identified associated with CV events in Group A: male gender, older age, chronic heart disease, cough, lymphocyte count <1.1 × 109/L at admission, blood urea nitrogen >7 mmol/L at admission, estimated glomerular filtration rate <90 ml/min/1.73 m2 at admission, activated partial thromboplastin time >37 S, D-dimer, and procalcitonin >0.5 mg/L. In our study, we found that CV events were significantly related with inferior prognosis (P < 0.001).
Conclusions: A new risk scoring system was designed in our study, which may be used as a predictive tool for CV complications among the patients with COVID-19 infection.
Fulltext Air Quality Prediction Using the Fractional Gradient-Based Recurrent Neural Network

Arora S, Sawaran Singh NS, Singh D, Rakesh Shrivastava R, Mathur T, Tiwari K, et al.

Comput Intell Neurosci, 2022;2022:9755422.
PMID: 36531923 DOI: 10.1155/2022/9755422

In this study, the air quality index (AQI) of Indian cities of different tiers is predicted by using the vanilla recurrent neural network (RNN). AQI is used to measure the air quality of any region which is calculated on the basis of the concentration of ground-level ozone, particle pollution, carbon monoxide, and sulphur dioxide in air. Thus, the present air quality of an area is dependent on current weather conditions, vehicle traffic in that area, or anything that increases air pollution. Also, the current air quality is dependent on the climate conditions and industrialization in that area. Thus, the AQI is history-dependent. To capture this dependency, the memory property of fractional derivatives is exploited in this algorithm and the fractional gradient descent algorithm involving Caputo's derivative has been used in the backpropagation algorithm for training of the RNN. Due to the availability of a large amount of data and high computation support, deep neural networks are capable of giving state-of-the-art results in the time series prediction. But, in this study, the basic vanilla RNN has been chosen to check the effectiveness of fractional derivatives. The AQI and gases affecting AQI prediction results for different cities show that the proposed algorithm leads to higher accuracy. It has been observed that the results of the vanilla RNN with fractional derivatives are comparable to long short-term memory (LSTM).