Project description:Circulating protein biomarkers provide information regarding pathways in heart failure (HF) and can add important value to clinicians. Advancements in proteomics allow researchers to measure a multitude of proteins simultaneously with excellent sensitivity and selectivity to detect low abundance proteins. This helps identify previously unrecognized pathways in HF and discover biomarkers and potential targets for HF therapies. Although several proteomic methods exist, including mass spectrometry, protein microarray, aptamer, and proximity extension assay-based techniques, each have their unique advantages. This paper provides an overview of the various proteomic methods, with examples of how each has contributed to understanding the pathways in HF.
Project description:Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as "the protein folding problem," has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with a summary of current challenges faced and an outlook on potential future directions.
Project description:Ticks and tick-borne diseases are significant public health concerns. Bioactive molecules in tick saliva facilitate prolonged blood-feeding and transmission of tick-borne pathogens to the vertebrate host. Alpha-gal syndrome (AGS), a newly reported food allergy, is believed to be induced by saliva proteins decorated with a sugar molecule, the oligosaccharide galactose-⍺-1,3-galactose (α-gal). This syndrome is characterized by an IgE antibody-directed hypersensitivity against α-gal. The α-gal antigen was discovered in the salivary glands and saliva of various tick species including, the Lone Star tick (Amblyomma americanum). The underlying immune mechanisms linking tick bites with α-gal-specific IgE production are poorly understood and are crucial to identify and establish novel treatments for this disease. This article reviews the current understanding of AGS and its involvement with tick species.
Project description:Collectively, viruses have the greatest genetic diversity on Earth, occupy extremely varied niches and are likely able to infect all living organisms. Viral infections are an important issue for human health and cause considerable economic losses when agriculturally important crops or husbandry animals are infected. The advent of metagenomics has provided a precious tool to study viruses by sampling them in natural environments and identifying the genomic composition of a sample. However, reaching a clear recognition and taxonomic assignment of the identified viruses has been hampered by the computational difficulty of these problems. In this perspective paper we examine the trends in current research for the identification of viral sequences in a metagenomic sample, pinpoint the intrinsic computational difficulties for the identification of novel viral sequences within metagenomic samples, and suggest possible avenues to overcome them.
Project description:Despite significant advances in the surgical and systemic therapy of colorectal cancer (CRC) in recent decades, recurrence rates remain high. Apart from microsatellite instability status, the decision to offer adjuvant chemotherapy to patients with CRC is solely based on clinicopathologic factors, which offer an inaccurate risk stratification of patients who derive benefit from adjuvant therapy. Owing to the recent improvements of molecular techniques, it has been possible to detect small allelic fractions of circulating tumor DNA (ctDNA), and therefore, to identify patients with minimal residual disease (MRD) after curative-intent therapies. The incorporation of ctDNA identifying MRD in clinical practice may dramatically change the standard of care of CRC, refining the selection of patients who are candidates for escalation and de-escalation of adjuvant chemotherapy, and even for organ-preservation strategies in rectal cancer. In the present review, we describe the current standard of care and the DNA sequencing methodologies and assays, present the data from completed clinical studies and list ongoing potential landmark clinical trials whose results are eagerly awaited, as well as the impact and perspectives for the near future. The discussed data bring optimism for the future of oncologic care through the hope of refined utilization of adjuvant therapies with higher efficacy and safety for patients with both localized and advanced CRC.
Project description:Mesothelioma comprises a group of rare cancers arising from the mesothelium of the pleura, peritoneum, tunica vaginalis testis and pericardium. Mesothelioma is generally associated with asbestos exposure and has a dismal prognosis, with few therapeutic options. Several next generation sequencing (NGS) experiments have been performed on mesothelioma arising at different sites. These studies highlight a genomic landscape mainly characterized by a high prevalence (>20%) of genomic aberrations leading to functional losses in oncosuppressor genes such as BAP1, CDKN2A, NF2, SETD2 and TP53. Nevertheless, to date, evidence of the effect of targeting these alterations with specific drugs is lacking. Conversely, 1-2% of mesothelioma might harbor activating mutations in oncogenes with specifically approved drugs. The goal of this review is to summarize NGS applications in mesothelioma and to provide insights into target therapy of mesothelioma guided by NGS.
Project description:High-throughput screening is an essential component of the toolbox of modern technologies that improve speed and efficiency in contemporary cancer drug development. This is particularly important as we seek to exploit, for maximum therapeutic benefit, the large number of new molecular targets emerging from the Human Genome Project and cancer genomics. Screening of diverse collections of low molecular weight compounds plays a key role in providing chemical starting points for iterative optimisation by medicinal chemistry. Examples of successful drug discovery programmes based on high-throughput screening are described, and these offer potential in the treatment of breast cancer and other malignancies.
Project description:In Mexico's territory, the center of origin and domestication of maize (Zea mays), there is a large phenotypic diversity of this crop. This diversity has been classified into "landraces." Previous studies have reported that genomic variation in Mexican maize is better explained by environmental factors, particularly those related with altitude, than by landrace. Still, landraces are extensively used by agronomists, who recognize them as stable and discriminatory categories for the classification of samples. In order to investigate the genomic foundation of maize landraces, we analyzed genomic data (35,909 SNPs from Illumina MaizeSNP50 BeadChip) obtained from 50 samples representing five maize landraces (Comiteco, Conejo, Tehua, Zapalote Grande, and Zapalote Chico), and searched for markers suitable for landrace assignment. Landrace clusters could not be identified taking all the genomic information, but they become manifest taking only a subset of SNPs with high FST among landraces. Discriminant analysis of principal components was conducted to classify samples using SNP data. Two classification analyses were done, first classifying samples by landrace and then by altitude category. Through this classification method, we identified 20 landrace-informative SNPs and 14 altitude-informative SNPs, with only 6 SNPs in common for both analyses. These results show that Mexican maize phenotypic diversity can be classified in landraces using a small number of genomic markers, given the fact that landrace genomic diversity is influenced by environmental factors as well as artificial selection due to bio-cultural practices.