Exploring variation in the fecal microbial communities of Kasaragod Dwarf and Holstein crossbred cattle.

The gut microbiota and its impact on health and nutrition in animals, including cattle has been of intense interest in recent times. Cattle, in particular indigenous varieties like Kasaragod Dwarf cow, have not received the due consideration given to other non-native cattle breeds, and the composition of their fecal microbiome is yet to be established. This study applied 16S rRNA high-throughput sequencing of fecal samples and compared the Kasaragod Dwarf with the highly prevalent Holstein crossbred cattle. Variation in their microbial composition was confirmed by marker gene-based taxonomic analysis. Principle Coordinate Analysis (PCoA) showed the distinct microbial architecture of the two cattle types. While the two cattle types possess unique signature taxa, in Kasaragod Dwarf cattle, many of the identified genera, including Anaerovibrio, Succinivibrio, Roseburia, Coprococcus, Paludibacter, Sutterella, Coprobacillus, and Ruminobacter, have previously been shown to be present in higher abundance in animals with higher feed efficiency. This is the first report of Kasaragod Dwarf cattle fecal microbiome profiling. Our findings highlight the predominance of specific taxa potentially associated with different fermentation products and feed efficiency phenotypes in Kasaragod Dwarf cattle compared to Holstein crossbred cattle.

Continuous Histone Replacement by Hira Is Essential for Normal Transcriptional Regulation and De Novo DNA Methylation during Mouse Oogenesis.

The integrity of chromatin, which provides a dynamic template for all DNA-related processes in eukaryotes, is maintained through replication-dependent and -independent assembly pathways. To address the role of histone deposition in the absence of DNA replication, we deleted the H3.3 chaperone Hira in developing mouse oocytes. We show that chromatin of non-replicative developing oocytes is dynamic and that lack of continuous H3.3/H4 deposition alters chromatin structure, resulting in increased DNase I sensitivity, the accumulation of DNA damage, and a severe fertility phenotype. On the molecular level, abnormal chromatin structure leads to a dramatic decrease in the dynamic range of gene expression, the appearance of spurious transcripts, and inefficient de novo DNA methylation. Our study thus unequivocally shows the importance of continuous histone replacement and chromatin homeostasis for transcriptional regulation and normal developmental progression in a non-replicative system in vivo.

MP4: a machine learning based classification tool for prediction and functional annotation of pathogenic proteins from metagenomic and genomic datasets.

Bacteria can exceptionally evolve and develop pathogenic features making it crucial to determine novel pathogenic proteins for specific therapeutic interventions. Therefore, we have developed a machine-learning tool that predicts and functionally classifies pathogenic proteins into their respective pathogenic classes. Through construction of pathogenic proteins database and optimization of ML algorithms, Support Vector Machine was selected for the model construction. The developed SVM classifier yielded an accuracy of 81.72% on the blind-dataset and classified the proteins into three classes: Non-pathogenic proteins (Class-1), Antibiotic Resistance Proteins and Toxins (Class-2), and Secretory System Associated and capsular proteins (Class-3). The classifier provided an accuracy of 79% on real dataset-1, and 72% on real dataset-2. Based on the probability of prediction, users can estimate the pathogenicity and annotation of proteins under scrutiny. Tool will provide accurate prediction of pathogenic proteins in genomic and metagenomic datasets providing leads for experimental validations. Tool is available at: http://metagenomics.iiserb.ac.in/mp4 .

Emerging evidence on Omicron (B.1.1.529) SARS-CoV-2 variant.

COVID's Omicron variant has sparked a slew of concerns across the globe. This review aims to provide a brief overview of what we know about the Omicron variant right now. The new variant has been discovered in 149 countries across all six World Health Organization (WHO) regions since its discovery in South Africa on November 24, 2021 and became the dominant variant in the country in less than 3 weeks. The WHO has warned that the B.1.1.529 variant is spreading at an unprecedented rate, and has urged countries to prepare for the worst. Over the course of this time, researchers from Africa and around the world have uncovered a wealth of information about the virus's epidemiology and biological properties. Case numbers are increasing exponentially in hard-hit areas such as South Africa, United Kingdom, and USA (overtaking the delta variant), implying that the variant is highly transmissible. Initial research has provided some insights into the efficacy of vaccines against the Omicron variant and whether it produces major illness, however, much remains unknown, and additional work is needed to investigate what the initial reports represent in real-world situations.

Molib: A machine learning based classification tool for the prediction of biofilm inhibitory molecules.

Identification of biofilm inhibitory small molecules appears promising for therapeutic intervention against biofilm-forming bacteria. However, the experimental identification of such molecules is a time-consuming task, and thus, the computational approaches emerge as promising alternatives. We developed the 'Molib' tool to predict the biofilm inhibitory activity of small molecules. We curated a training dataset of biofilm inhibitory molecules, and the structural and chemical features were used for feature selection, followed by algorithms optimization and building of machine learning-based classification models. On five-fold cross validation, Random Forest-based descriptor, fingerprint and hybrid classification models showed accuracies of 0.93, 0.88 and 0.90, respectively. The performances of all models were evaluated on two different validation datasets including biofilm inhibitory and non-inhibitory molecules, attesting to its accuracy (≥ 0.90). The Molib web server would serve as a highly useful and reliable tool for the prediction of biofilm inhibitory activity of small molecules.

Development and characterization of non-coding RNA based simple sequence repeat markers in Capsicum species.

Plant growth and development are largely regulated by non-coding RNAs (ncRNA); thus ncRNA based markers would be rewarding in molecular breeding. In the present study, for the first time we developed total 623 ncRNA based SSRs including 119 microRNASSRs (miRNASSRs) and 504 long non-coding RNASSRs (lncRNASSRs) distributed across 12 Capsicum chromosomes. Out of 623 ncRNASSRs, 120 (including 60 each miRNASSRs and lncRNASSRs) were used for genotyping of 96 Capsicum accessions belonging to C. annuum, C. chinense and C. frutescens; and 75% SSRs were polymorphic. Model-based and distance-based cluster analyses identified three species specific clusters, i.e. cluster-I (C. annuum), cluster-II (C. frutescens) and cluster-III (C. chinense); therefore, these SSRs may have a potential role to play in interspecific Capsicum breeding. Tissue specific expression of SSR containing ncRNAs and versatile functions of their targets suggested the usefulness of SSRs for mapping of genes/QTLs and breeding of wide range of traits in Capsicum.

Influence of surface modification of titanium implants on improving osseointegration: An in vitro study.

STATEMENT OF PROBLEM: The effect of aging and the surface treatment of implants on osseointegration needs to be evaluated. PURPOSE: The purpose of this in vitro study was to evaluate the effects of aging and the surface treatment of titanium with ultraviolet (UV) radiation and fibroblast growth factor (FGF) on hydrophilicity and cell growth and thus on osseointegration. MATERIAL AND METHODS: A total of 28 specimens were divided into 2 groups to measure hydrophilicity (n=14) and cell growth (n=14). Each group was further divided into 4 groups according to surface modification. These include the control group (CG) (nascent specimens), aged group (AG) (nascent specimens aged for 4 weeks), photofunctionalized group (PG) (aged specimens UV-A treated), and mimed group (MG) (aged specimens UV-A and FGF2 treated). The PG and MG specimens were treated with UV-A light for 40 minutes. The biomimetic surface modification was performed for MG. Hydrophilicity was measured by using the contact angle in relation to the surface of titanium disks with the help of a drop shape analyzing device (KRUSS), and cell growth was measured by calculating the number of stem cells per cm2 with the help of a scanning electron microscope (SEM). The data obtained were subjected to statistical analysis with a statistical software program (α=.05). RESULTS: The lowest contact angle values were found in PG (13.52 ±0.90 degrees) and the highest in AG (70.54 ±1.72 degrees). The highest number of cells per cm2 (2880 ±99.33) were found for MG, and the lowest number of cells per cm2 (760 ±9.17) for AG. CONCLUSIONS: Aging decreased the hydrophilicity and cell adhesion, migration, and growth on the titanium surface. UV treatment improved the hydrophilicity, cell adhesion, migration, and growth for both CG and AG. FGF2 treatment increased the cell adhesion, migration, and growth for CG, AG, and PG.

Mechanistic elucidation of amphetamine metabolism by tyramine oxidase from human gut microbiota using molecular dynamics simulations.

The human gut harbors diverse bacterial species in the gut, which play an important role in the metabolism of food and host health. Recent studies have also revealed their role in altering the pharmacological properties and efficacy of oral drugs through promiscuous metabolism. However, the atomistic details of the enzyme-drug interactions of gut bacterial enzymes which can potentially carry out the metabolism of drug molecules are still scarce. A well-known example is the FDA drug amphetamine (a central nervous system stimulant), which has been predicted to undergo promiscuous metabolism by gut bacteria. Therefore, to understand the atomistic details and energy landscape of the gut microbial enzyme-mediated metabolism of this drug, molecular dynamics studies were performed. It was observed that amphetamine binds to tyramine oxidase from the Escherichia coli strain present in the human gut microbiota at the binding site harboring polar and nonpolar amino acids. The stability analysis of amphetamine at the binding site showed that the binding is stable and the free energy for the binding of amphetamine was found to be ~ -51.71 kJ/mol. The insights provided by this study on promiscuous metabolism of amphetamine by a gut enzyme will be very useful to improve the efficacy of the drug.

Oto-acoustic emissions and brainstem evoked response audiometry in patients of tinnitus with normal hearing.

INTRODUCTION: Tinnitus is defined as the perception of sound that results completely from activity within the nervous system without any corresponding mechanical, vibratory activity within the cochlea, and not related to external stimulation of any kind. It disrupts the daily life of 1 out of every 200 adults. The source of tinnitus generation is not limited to the peripheral auditory system. However, there are abnormalities seen in BERA in tinnitus patients depicting auditory pathway involvement. Oto-acoustic emissions are mechanical vibrations generated in the cochlea, which are evaluated by TEOAE and DPOAE whereas BERA evaluates both cochlea and brainstem auditory pathway for any conduction abnormalities. The aim of the study is to analyze the changes in OAE and BERA in patients suffering from tinnitus with normal hearing, which may help us to understand the patho-physiology of tinnitus. METHODS: This is a prospective study conducted in a tertiary care hospital in Northern India between 1st December 2015 to 31st July 2017. All patients of tinnitus with normal hearing were included in the study group, whereas Individuals with normal hearing with no other ear complaints were included in control group. Total 160 Ears were evaluated with 80 ears in both study and control group each. Patients with PTA >25dB, age >55 years or any chronic medical illness were excluded from the study. RESULTS: 80 individuals (46 Males and 34 Females) were divided into study and control Group (80 Ears each). Tinnitus was bilateral in 28 subjects (53.84%) and unilateral in 24 subjects (46.16%). Both control and study group showed significant difference in TEOAE and DPOAE study. In TEOAE, 8 (10%) ears in control group and 30 ears (37.5%) in study group showed test result as REFER whereas in DPOAE 10 (12.5%) ears in control group and 35 (43.8%) ears showed test result as REFER. All these result were statistically significant. In BERA the latency of wave I was significantly prolonged in study group as compared to control group, while difference between all other parameters between the two groups was insignificant. CONCLUSIONS: There were various significant abnormalities seen in parameters of Oto-Acoustic Emissions (OAE) and Brainstem Evoked Response Audiometry (BERA). So these tests should be included in the test battery for the screening of patients complaining of tinnitus even with normal hearing.

Comparative evaluation of torque prescription of commercially available 018Roth and 022MBT PEA brands in maxillary anterior teeth.

BACKGROUND: Torque is an important component of preadjusted edgewise prescriptions to achieve ideal/optimum tooth position and more so in aesthetics sensitive maxillary anterior teeth. Thus, the need to audit available commercial brands of 018Roth and 022MBT was felt and in vitro analysis of eight brands namely 3M Unitek, Dentaurum, d-tech, IMD Medical, Libral Leone, Modern Orthodontics, Ormco and Ortho Organizer, was carried out. METHODS: The method involved perfect superimposition of two standardized images: one delineating facial axis of the tooth with stainless steel straight wire and other with full dimension arch wire engagement in bracket. The two images were superimposed and opacity of one of the images altered using Adobe Photoshop software to reveal the position of two wires. The angle obtained between two wires gave a direct read-out of torque expression. The comparison was statistically done with one-way ANOVA and Tukey's HSD test. RESULTS: In 018Roth group, IMD Medical did not show any significant difference from the standard while comparison independent of standard showed that IMD, Modern Orthodontics and Ormco were not significantly different from each other. In 022MBT group, IMD Medical, d-tech, 3M Unitek and Modern Orthodontics did not have statistically significant difference from and independent of standard. CONCLUSION: Significant variations from the standard values of both 018 Roth and 022 MBT exist in the market products and thus selection of product must be based on proper guidance in addition to clinical acumen/experience. Also the methodology provides easy to use, inexpensive set-up in the clinical settings.

Mechanistic and structural insight into promiscuity based metabolism of cardiac drug digoxin by gut microbial enzyme.

The recent advances in microbiome studies have revealed the role of gut microbiota in altering the pharmacological properties of oral drugs, which contributes to patient-response variation and undesired effect of the drug molecule. These studies are essential to guide us for achieving the desired efficacy and pharmacological activity of the existing drug molecule or for discovering novel and more effective therapeutics. However, one of the main limitations is the lack of atomistic details on the binding and metabolism of these drug molecules by gut-microbial enzymes. Therefore, in this study, for a well-known and important FDA-approved cardiac glycoside drug, digoxin, we report the atomistic details and energy economics for its binding and metabolism by the Cgr2 protein of Eggerthella lenta DSM 2243. It was observed that the binding pocket of digoxin to Cgr2 primarily involved the negatively charged polar amino acids and a few non-polar hydrophobic residues. The drug digoxin was found to bind Cgr2 at the same binding site as that of fumarate, which is the proposed natural substrate. However, digoxin showed a much lower binding energy (17.75 ± 2 Kcal mol-1 ) than the binding energy (42.17 ± 2 Kcal mol-1 ) of fumarate. This study provides mechanistic insights into the structural and promiscuity-based metabolism of widely used cardiac drug digoxin and presents a methodology, which could be useful to confirm the promiscuity-based metabolism of other orally administrated drugs by gut microbial enzymes and also help in designing strategies for improving the efficacy of the drugs.

Gut microbiome contributes to impairment of immunity in pulmonary tuberculosis patients by alteration of butyrate and propionate producers.

Tuberculosis (TB) is primarily associated with decline in immune health status. As gut microbiome (GM) is implicated in the regulation of host immunity and metabolism, here we investigate GM alteration in TB patients by 16S rRNA gene and whole-genome shotgun sequencing. The study group constituted of patients with pulmonary TB and their healthy household contacts as controls (HCs). Significant alteration of microbial taxonomic and functional capacity was observed in patients with active TB as compared to the HCs. We observed that Prevotella and Bifidobacterium abundance were associated with HCs, whereas butyrate and propionate-producing bacteria like Faecalibacterium, Roseburia, Eubacterium and Phascolarctobacterium were significantly enriched in TB patients. Functional analysis showed reduced biosynthesis of vitamins and amino acids in favour of enriched metabolism of butyrate and propionate in TB subjects. The TB subjects were also investigated during the course of treatment, to analyse the variation of GM. Although perturbation in microbial composition was still evident after a month's administration of anti-TB drugs, significant changes were observed in metagenome gene pool that pointed towards recovery in functional capacity. Therefore, the findings from this pilot study suggest that microbial dysbiosis may contribute to pathophysiology of TB by enhancing the anti-inflammatory milieu in the host.

Gut Microbial Dysbiosis in Indian Children with Autism Spectrum Disorders.

Autism spectrum disorder (ASD) is a term associated with a group of neurodevelopmental disorders. The etiology of ASD is not yet completely understood; however, a disorder in the gut-brain axis is emerging as a prominent factor leading to autism. To identify the taxonomic composition and markers associated with ASD, we compared the fecal microbiota of 30 ASD children diagnosed using Childhood Autism Rating Scale (CARS) score, DSM-5 approved AIIMS-modified INCLEN Diagnostic Tool for Autism Spectrum Disorder (INDT-ASD), and Indian Scale for Assessment of Autism (ISAA) tool, with family-matched 24 healthy children from Indian population using next-generation sequencing (NGS) of 16S rRNA gene amplicon. Our study showed prominent dysbiosis in the gut microbiome of ASD children, with higher relative abundances of families Lactobacillaceae, Bifidobacteraceae, and Veillonellaceae, whereas the gut microbiome of healthy children was dominated by the family Prevotellaceae. Comparative meta-analysis with a publicly available dataset from the US population consisting of 20 ASD and 20 healthy control samples from children of similar age, revealed a significantly high abundance of genus Lactobacillus in ASD children from both the populations. The results reveal the microbial dysbiosis and an association of selected Lactobacillus species with the gut microbiome of ASD children.

Prediction of anti-inflammatory proteins/peptides: an insilico approach.

BACKGROUND: The current therapy for inflammatory and autoimmune disorders involves the use of nonspecific anti-inflammatory drugs and other immunosuppressant, which are often accompanied with potential side effects. As an alternative therapy, anti-inflammatory peptides are recently being exploited as anti-inflammatory agents for treatment of various inflammatory diseases such as Alzheimer's disease and rheumatoid arthritis. Thus, understanding the correlation between amino acid sequence and its potential anti-inflammatory property is of great importance for the discovery of novel and efficient anti-inflammatory peptide-based therapeutics. METHODS: In this study, we have developed a prediction tool for the classification of peptides as anti-inflammatory epitopes or non anti-inflammatory epitopes. The training was performed using experimentally validated epitopes obtained from Immune epitope database and analysis resource database. Different sequence-based features and their hybrids with motif information were employed for development of support vector machine-based machine learning models. Similarly, machine learning models were also constructed using random forest. RESULTS: The composition and terminal residue conservation analysis of peptides revealed the dominance of leucine, serine, tyrosine and arginine residues in anti-inflammatory epitopes as compared to non anti-inflammatory epitopes. Similarly, the anti-inflammatory epitopes specific motifs were found to be rich in hydrophobic and polar residues. The hybrid of tripeptide composition-based support vector machine model and motif yielded the best performance on 10-fold cross validation with an accuracy of 78.1% and MCC of 0.58. The same displayed an accuracy of 72% and MCC of 0.45 on validation dataset, rejecting any possibility of over-fitting. The tripeptide composition-based random forest model displayed an accuracy of 0.8 and MCC of 0.59 on 10-fold cross validation, however, the accuracy (0.68) and MCC (0.31) was lower as compared to support vector machine model on validation dataset. Thus, the support vector machine model is implemented as the default model and an additional option of using the random forest model is provided. CONCLUSION: The prediction models along with tools for epitope mapping and similarity search have been provided as a web server which is freely accessible at http://metagenomics.iiserb.ac.in/antiinflam/ .

Prediction of peptidoglycan hydrolases- a new class of antibacterial proteins.

BACKGROUND: The efficacy of antibiotics against bacterial infections is decreasing due to the development of resistance in bacteria, and thus, there is a need to search for potential alternatives to antibiotics. In this scenario, peptidoglycan hydrolases can be used as alternate antibacterial agents due to their unique property of cleaving peptidoglycan cell wall present in both gram-positive and gram-negative bacteria. Along with a role in maintaining overall peptidoglycan turnover in a cell and in daughter cell separation, peptidoglycan hydrolases also play crucial role in bacterial pathophysiology requiring development of a computational tool for the identification and classification of novel peptidoglycan hydrolases from genomic and metagenomic data. RESULTS: In this study, the known peptidoglycan hydrolases were divided into multiple classes based on their site of action and were used for the development of a computational tool 'HyPe' for identification and classification of novel peptidoglycan hydrolases from genomic and metagenomic data. Various classification models were developed using amino acid and dipeptide composition features by training and optimization of Random Forest and Support Vector Machines. Random Forest multiclass model was selected for the development of HyPe tool as it showed up to 71.12 % sensitivity, 99.98 % specificity, 99.55 % accuracy and 0.80 MCC in four different classes of peptidoglycan hydrolases. The tool was validated on 24 independent genomic datasets and showed up to 100 % sensitivity and 0.94 MCC. The ability of HyPe to identify novel peptidoglycan hydrolases was also demonstrated on 24 metagenomic datasets. CONCLUSIONS: The present tool helps in the identification and classification of novel peptidoglycan hydrolases from complete genomic or metagenomic ORFs. To our knowledge, this is the only tool available for the prediction of peptidoglycan hydrolases from genomic and metagenomic data. AVAILABILITY: http://metagenomics.iiserb.ac.in/hype/ and http://metabiosys.iiserb.ac.in/hype/ .

ProInflam: a webserver for the prediction of proinflammatory antigenicity of peptides and proteins.

BACKGROUND: Proinflammatory immune response involves a complex series of molecular events leading to inflammatory reaction at a site, which enables host to combat plurality of infectious agents. It can be initiated by specific stimuli such as viral, bacterial, parasitic or allergenic antigens, or by non-specific stimuli such as LPS. On counter with such antigens, the complex interaction of antigen presenting cells, T cells and inflammatory mediators like IL1α, IL1ß, TNFα, IL12, IL18 and IL23 lead to proinflammatory immune response and further clearance of infection. In this study, we have tried to establish a relation between amino acid sequence of antigen and induction of proinflammatory response. RESULTS: A total of 729 experimentally-validated proinflammatory and 171 non-proinflammatory epitopes were obtained from IEDB database. The A, F, I, L and V amino acids and AF, FA, FF, PF, IV, IN dipeptides were observed as preferred residues in proinflammatory epitopes. Using the compositional and motif-based features of proinflammatory and non-proinflammatory epitopes, we have developed machine learning-based models for prediction of proinflammatory response of peptides. The hybrid of motifs and dipeptide-based features displayed best performance with MCC = 0.58 and an accuracy of 87.6 %. CONCLUSION: The amino acid sequence-based features of peptides were used to develop a machine learning-based prediction tool for the prediction of proinflammatory epitopes. This is a unique tool for the computational identification of proinflammatory peptide antigen/candidates and provides leads for experimental validations. The prediction model and tools for epitope mapping and similarity search are provided as a comprehensive web server which is freely available at http://metagenomics.iiserb.ac.in/proinflam/ and http://metabiosys.iiserb.ac.in/proinflam/ .

Public Health, Hypertension, and the Emergency Department.

Hypertension (HTN) is the most common cardiovascular disease worldwide and is associated with severe long-term morbidity when not treated appropriately. Despite this, blood pressure (BP) control remains suboptimal, particularly among underserved populations and those who rely on emergency departments (EDs) as a source of primary care. ED providers encounter patients with severely elevated BP daily, and yet adherence to minimal standards of BP reassessment and referral to outpatient medical care, as recommended by the American College of Emergency Physicians, is limited. Barriers such as provider knowledge deficits, resource constraints, and negative attitudes towards patients who utilize EDs for nonurgent complaints are compounded by perceptions of HTN as a condition that can only be addressed in a primary care setting to contribute to this. Efforts to reduce this gap must go beyond government mandates to address systemic issues including access to care and payment models to encourage health promotion. Additionally, individual physician behavior can be shifted through targeted education, financial incentives, and the accumulation of high-quality evidence to encourage more proactive approaches to the management of uncontrolled HTN in the ED.

Woods: A fast and accurate functional annotator and classifier of genomic and metagenomic sequences.

Functional annotation of the gigantic metagenomic data is one of the major time-consuming and computationally demanding tasks, which is currently a bottleneck for the efficient analysis. The commonly used homology-based methods to functionally annotate and classify proteins are extremely slow. Therefore, to achieve faster and accurate functional annotation, we have developed an orthology-based functional classifier 'Woods' by using a combination of machine learning and similarity-based approaches. Woods displayed a precision of 98.79% on independent genomic dataset, 96.66% on simulated metagenomic dataset and >97% on two real metagenomic datasets. In addition, it performed >87 times faster than BLAST on the two real metagenomic datasets. Woods can be used as a highly efficient and accurate classifier with high-throughput capability which facilitates its usability on large metagenomic datasets.

Role of invertase activity in processing quality of potatoes: Effect of storage temperature and duration.

Invertase activity and processing attributes of three potato cultivars were studied to find the reason for deterioration of processing quality during their prolonged storage in commercial cold stores (4°C) as compared to elevated temperature storage (12 ± 0.5°C), with CIPC {Isopropyl-N-(3-Cholorophenyl) carbamate}. Lower storage temperature (4°C) tended to be more effective in increasing invertase activity of potato tubers than elevated temperature. Non-processing cultivar viz., Kufri Pukhraj resulted in accumulation of more invertase activity than relatively two processing cultivars. Kufri Chipsona-1 and Kufri Chipsona-3 at 12 ± 0.5°C possessed basal invertase activity ranging from 39.3 to 79.8 and 54.1 to 93.8 (pmoles hexose h⁻¹ g⁻¹ f.wt.) respectively, during two years. Total invertase activity at 4°C increased abruptly and remained high from 30 to 60 days of storage. The activity progressively reached 90.6 to 106.6 and 81.4 to 101.3 during both the years respectively, after 60 days of storage to that observed initially. Reducing sugar content increased from 23.3 to 105.7 and 389.0 to 1138.2 (mg 100g⁻¹ f.wt.) after 90 days of storage at 12 ± 0.5°C and 4°C, respectively. Studies concluded that basal and total invertase, were responsible for cold-induced sweetening and resulted in deterioration of processing quality of potatoes during storage at 4°C. Since this activity is low at 12 ± 0.5°C, the processing traits remained acceptable to industry and consumers.

Variation in biochemical parameters in different parts of potato tubers for processing purposes.

The present study was conducted to estimate the variation in bio-chemical parameters among eight different parts viz. bud end cortex, bud end medulla, central cortex, central medulla, pith, stem end cortex, stem end medulla and peel of potato tuber of processing varieties. Concentration of dry matter, reducing sugar, sucrose and starch content were higher in cortical region than in medullar region of stem end, bud end and central portion. Variety Kufri Chipsona-1 had maximum dry matter content in stem end cortex (SEC 30.34 %), followed by Kufri Frysona (SEC 27.71 %). Mean reducing sugar values were comparatively more in bud end cortex (BEC 111.3 mg/100 g Fresh Weight) and lowest in stem end medulla (SEM 44.05 mg/100 g FW). Bio-chemical contents varied considerably within different parts of tuber as well as in different genotypes. The information generated in this study can help processors for effective utilization of potato for various types of processing products viz., chips and French fries.