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Deconvoluting mixture samples is one of the most challenging problems confronting DNA forensic laboratories. Efforts have been made to provide solutions regarding mixture interpretation. The probabilistic interpretation of Short Tandem Repeat (STR) profiles has increased the number of complex mixtures that can be analyzed. A portion of complex mixture profiles, particularly for mixtures with a high number of contributors, are still being deemed uninterpretable. Novel forensic markers, such as Single Nucleotide Variants (SNV) and microhaplotypes, also have been proposed to allow for better mixture interpretation. However, these markers have both a lower discrimination power compared with STRs and are not compatible with CODIS or other national DNA databanks worldwide. The short-read sequencing (SRS) technologies can facilitate mixture interpretation by identifying intra-allelic variations within STRs. Unfortunately, the short size of the amplicons containing STR markers and sequence reads limit the alleles that can be attained per STR. The latest long-read sequencing (LRS) technologies can overcome this limitation in some samples in which larger DNA fragments (including both STRs and SNVs) with definitive phasing are available. Based on the LRS technologies, this study developed a novel CODIS compatible forensic marker, called a macrohaplotype, which combines a CODIS STR and flanking variants to offer extremely high number of haplotypes and hence very high discrimination power per marker. The macrohaplotype will substantially improve mixture interpretation capabilities. Based on publicly accessible data, a panel of 20 macrohaplotypes with sizes of ~ 8 k bp and the maximum high discrimination powers were designed. The statistical evaluation demonstrates that these macrohaplotypes substantially outperform CODIS STRs for mixture interpretation, particularly for mixtures with a high number of contributors, as well as other forensic applications. Based on these results, efforts should be undertaken to build a complete workflow, both wet-lab and bioinformatics, to precisely call the variants and generate the macrohaplotypes based on the LRS technologies.

Applications of bioinformatic methods and high-throughput sequencing techniques have increased exponentially over the last decade, transforming the way we understand complex malignancies such as breast cancer. In this chapter, an overview of recent advances in molecular research in breast cancer using emerging bioinformatics methods is presented. Learnings from scientific studies that have successfully integrated and interpreted massive amounts of data generated from various platforms (multi-omics data) using bioinformatics approaches are also outlined. Additionally, pan-cancer studies that help identify the differences and commonalities across multiple cancers are reviewed. We also discuss bioinformatics applications that transform the way we decipher the OncoGenomic landscape of breast cancer. Finally, this study also summarizes current publicly available bioinformatics tools and databases for breast cancer research.

Background The biguanide metformin has been shown to not only reduce circulating glucose levels but also suppress in vitro and in vivo growth of prostate cancer. However, the mechanisms underlying the anti-tumor effects of metformin in advanced prostate cancers are not fully understood. The goal of the present study was to define the signaling pathways regulated by metformin in androgen-receptor (AR) positive, castration-resistant prostate cancers. Methods Our group used RNA sequencing (RNA-seq) to examine genes regulated by metformin within the C4–2 human prostate cancer cell line. Western blot analysis and quantitative RT-PCR were used to confirm alterations in gene expression and further explore regulation of protein expression by metformin. Results Data from the RNA-seq analysis revealed that metformin alters the expression of genes products involved in metabolic pathways, the spliceosome, RNA transport, and protein processing within the endoplasmic reticulum. Gene products involved in ErbB, insulin, mTOR, TGF-β, MAPK, and Wnt signaling pathways are also regulated by metformin. A subset of metformin-regulated gene products were genes known to be direct transcriptional targets of p53 or AR. Western blot analyses and quantitative RT-PCR indicated these alterations in gene expression are due in part to metformin-induced reductions in AR mRNA and protein levels. Conclusions Together, our results suggest metformin regulates multiple pathways linked to tumor growth and progression within advanced prostate cancer cells.

Despite the benefits of quantitative data generated by massively parallel sequencing, resolving mitotypes from mixtures occurring in certain ratios remains challenging. In this study, a bioinformatic mixture deconvolution method centered on population-based phasing was developed and validated. The method was first tested on 270 in silico two-person mixtures varying in mixture proportions. An assortment of external reference panels containing information on haplotypic variation (from similar and different haplogroups) was leveraged to assess the effect of panel composition on phasing accuracy. Building on these simulations, mitochondrial genomes from the Human Mitochondrial DataBase were sourced to populate the panels and key parameter values were identified by deconvolving an additional 7290 in silico two-person mixtures. Finally, employing an optimized reference panel and phasing parameters, the approach was validated with in vitro two-person mixtures with differing proportions. Deconvolution was most accurate when the haplotypes in the mixture were similar to haplotypes present in the reference panel and when the mixture ratios were neither highly imbalanced nor subequal (e.g., 4:1). Overall, errors in haplotype estimation were largely bounded by the accuracy of the mixture’s genotype results. The proposed framework is the first available approach that automates the reconstruction of complete individual mitotypes from mixtures, even in ratios that have traditionally been considered problematic.

Unique molecular identifiers (UMIs) are a promising approach to contend with errors generated during PCR and massively parallel sequencing (MPS). With UMI technology, random molecular barcodes are ligated to template DNA molecules prior to PCR, allowing PCR and sequencing error to be tracked and corrected bioinformatically. UMIs have the potential to be particularly informative for the interpretation of short tandem repeats (STRs). Traditional MPS approaches may simply lead to the observation of alleles that are consistent with the hypotheses of stutter, while with UMIs stutter products bioinformatically may be re-associated with their parental alleles and subsequently removed. Herein, a bioinformatics pipeline named strumi is described that is designed for the analysis of STRs that are tagged with UMIs. Unlike other tools, strumi is an alignment-free machine learning driven algorithm that clusters individual MPS reads into UMI families, infers consensus super-reads that represent each family and provides an estimate the resulting haplotype's accuracy. Super-reads, in turn, approximate independent measurements not of the PCR products, but of the original template molecules, both in terms of quantity and sequence identity. Provisional assessments show that naive threshold-based approaches generate super-reads that are accurate (∼97 % haplotype accuracy, compared to ∼78 % when UMIs are not used), and the application of a more nuanced machine learning approach increases the accuracy to ∼99.5 % depending on the level of certainty desired. With these features, UMIs may greatly simplify probabilistic genotyping systems and reduce uncertainty. However, the ability to interpret alleles at trace levels also permits the interpretation, characterization and quantification of contamination as well as somatic variation (including somatic stutter), which may present newfound challenges.

Blood and semen are important body-fluids that carry exosomes for bioinformation transmission. Therefore, characterization of their proteomes is necessary for understanding body-fluid-specific physiologic and pathophysiologic functions. Using systematic multifactorial proteomic profiling, we characterized the proteomes of exosomes and exosome-free fractions from autologous blood and semen from three HIV-uninfected and three HIV-infected participants (total of 24 samples). We identified exosome-based protein signatures specific to blood and semen along with HIV-induced tissue-dependent proteomic perturbations. We validated our findings with samples from 16 additional donors and showed that unlike blood exosomes (BE), semen exosomes (SE) are enriched in clusterin. SE but not BE promote Protein·Nucleic acid binding and increase cell adhesion irrespective of HIV infection. This is the first comparative study of the proteome of autologous BE and SE. The proteins identified may be developed as biomarkers applicable to different fields of medicine, including reproduction and infectious diseases.

Short tandem repeats of the nuclear genome have been the preferred markers for analyzing forensic DNA mixtures. However, when nuclear DNA in a sample is degraded or limited, mitochondrial DNA (mtDNA) markers provide a powerful alternative. Though historically considered challenging, the interpretation and analysis of mtDNA mixtures have recently seen renewed interest with the advent of massively parallel sequencing. However, there are only a few software tools available for mtDNA mixture interpretation. To address this gap, the Mitochondrial Mixture Deconvolution and Interpretation Tool (MMDIT) was developed. MMDIT is an interactive application complete with a graphical user interface that allows users to deconvolve mtDNA (whole or partial genomes) mixtures into constituent donor haplotypes and estimate random match probabilities on these resultant haplotypes. In cases where deconvolution might not be feasible, the software allows mixture analysis directly within a binary framework (i.e. qualitatively, only using data on allele presence/absence). This paper explains the functionality of MMDIT, using an example of an in vitro two-person mtDNA mixture with a ratio of 1:4. The uniqueness of MMDIT lies in its ability to resolve mixtures into complete donor haplotypes using a statistical phasing framework before mixture analysis and evaluating statistical weights employing a novel graph algorithm approach. MMDIT is the first available open-source software that can automate mtDNA mixture deconvolution and analysis. The MMDIT web application can be accessed online at https://www.unthsc.edu/mmdit/. The source code is available at https://github.com/SammedMandape/MMDIT_UI and archived on zenodo (https://doi.org/10.5281/zenodo.4770184).

Since 2013, STRait Razor has enabled analysis of massively parallel sequencing (MPS) data from various marker systems such as short tandem repeats, single nucleotide polymorphisms, insertion/deletions, and mitochondrial DNA. In this paper, STRait Razor Online (SRO), available at https://www.unthsc.edu/straitrazor, is introduced as an interactive, Shiny-based user interface for primary analysis of MPS data and secondary analysis of STRait Razor haplotype pileups. This software can be accessed from any common browser via desktop, tablet, or smartphone device. SRO is available also as a standalone application and open-source R script available at https://github.com/ExpectationsManaged/STRaitRazorOnline. The local application is capable of batch processing of both fastq files and primary analysis output. Processed batches generate individual report folders and summary reports at the locus- and haplotype-level in a matter of minutes. For example, the processing of data from ∼700 samples generated with the ForenSeq Signature Preparation Kit from allsequences.txt to a final table can be performed in ∼40 min whereas the Excel-based workbooks can take 35-60 h to compile a subset of the tables generated by SRO. To facilitate analysis of single-source, reference samples, a preliminary triaging system was implemented that calls potential alleles and flags loci suspected of severe heterozygote imbalance. When compared to published, manually curated data sets, 98.72 % of software-assigned allele calls without manual interpretation were consistent with curated data sets, 0.99 % loci were presented to the user for interpretation due to heterozygote imbalance, and the remaining 0.29 % of loci were inconsistent due to the analytical thresholds used across the studies.

Impaired colon motility is one of the leading problems associated with inflammatory bowel disease (IBD). An expanding body of evidence supports the role of microbiome in normal gut function and in progression of IBD. The objective of this work is to determine whether diseased full thickness colon specimens, including the neuromuscular region (critical for colon motility function), contain specific oral and gut pathogens. In addition, we compared the differences in colon microbiome between Caucasians (CA) and African Americans (AA). Thirty-nine human full thickness colon (diseased colon and adjacent healthy colon) specimens were collected from Crohn's Colitis (CC) or Ulcerative Colitis (UC) patients while they underwent elective colon surgeries. We isolated and analyzed bacterial ribosomal RNA (rRNA) from colon specimens by amplicon sequencing of the 16S rRNA gene region. The microbiome proportions were quantified into Operational Taxonomic Units (OTUs) by analysis with Quantitative Insights Into Microbial ecology (QIIME) platform. Two hundred twenty-eight different bacterial species were identified by QIIME analysis. However, we could only decipher the species name of fifty-three bacteria. Our results show that proportion of non-detrimental bacteria in CC or UC colon samples were altered compared to adjacent healthy colon specimens. We further show, for the first time in full thickness colon specimens, that microbiome of CC and UC diseased specimens is dominated by putative oral pathogens belonging to the Phyla Firmicutes (Streptococcus, Staphylococcus, Peptostreptococcus), and Fusobacteria (Fusobacterium). In addition, we have identified patterns of differences in microbiome levels between CA and AA specimens with potential implications for health disparities research. Overall, our results suggest a significant association between oral and gut microbes in the modulation of colon motility in colitis patients.

The protozoan parasite Trypanosoma cruzi, the causative agent of Chagas disease, causes severe morbidity and mortality in afflicted individuals. About 30% of T. cruzi-infected individuals present with cardiac, gastrointestinal tract, and/or neurological disorders. Megacolon, one of the major pathologies of Chagas disease, is accompanied by gastrointestinal motility disorders. The molecular mechanism of T. cruzi-mediated megacolon in Chagas disease is currently unknown. To decipher the molecular mechanism of T. cruzi-induced alteration in the colon during the early infection phase, we exposed primary human colonic epithelial cells (HCoEpiC) to invasive T. cruzi trypomastigotes at multiple time points to determine changes in the phosphoprotein networks in the cells following infection using proteome profiler Human phospho-kinase arrays. We found significant changes in the phosphorylation pattern that can mediate cellular deregulations in colonic epithelial cells after infection. We detected a significant increase in the levels of phosphorylated heat shock protein (p-HSP) 27 and transcription factors that regulate various cellular functions, including c-Jun and CREB. Our study confirmed significant upregulation of phospho (p-) Akt S473, p-JNK, which may directly or indirectly modulate CREB and c-Jun phosphorylation, respectively. We also observed increased levels of phosphorylated CREB and c-Jun in the nucleus. Furthermore, we found that p-c-Jun and p-CREB co-localized in the nucleus at 180 minutes post infection, with a maximum Pearson correlation coefficient of 0.76±0.02. Increased p-c-Jun and p-CREB have been linked to inflammatory and profibrotic responses. T. cruzi infection of HCoEpiC induces an increased expression of thrombospondin-1 (TSP-1), which is fibrogenic at elevated levels. We also found that T. cruzi infection modulates the expression of NF-kB and JAK2-STAT1 signaling molecules which can increase pro-inflammatory flux. Bioinformatics analysis of the phosphoprotein networks derived using the phospho-protein data serves as a blueprint for T. cruzi-mediated cellular transformation of primary human colonic cells during the early phase of T. cruzi infection., Author summary Trypanosoma cruzi is a hemoflagellate that is now considered a global health threat in all industrialized regions of the world. Some chagasic patients present with digestive, neurological, and/or cardiac disorders. The mechanisms of T. cruzi-induced pathology remain to be elucidated. In this study, we challenged primary human colonic epithelial cells with T. cruzi and evaluated changes in the phosphorylated kinases and phosphoprotein levels that may induce cellular and molecular alterations leading to cellular transformations during the early phase of infection. The parasite induced significant increases in levels of phosphorylated kinases and phosphoproteins that govern multiple cellular pathways associated with immunological, stress, neuronal, and intercellular interactions as well as fibrogenic responses. The parasite also enhanced the levels of p-AKT, p-HSP27, p-JNK, and downstream transcription factors like p-c-Jun and p-CREB during the early infection phase. Additionally, we observed that the phosphorylated transcription factors are translocated to and colocalized in the nucleus in a time-dependent manner. These transcription factors regulate the expression of genes, including genes encoding extracellular matrix proteins, which play a role in the onset of colon pathology observed in some chagasic patients. Our study provides novel insights into the interactome that occurs during acute phase of T. cruzi infection of primary human colon cells.

The molecular mechanisms of Trypanosoma cruzi induced cardiac fibrosis remains to be elucidated. Primary human cardiomyoctes (PHCM) exposed to invasive T. cruzi trypomastigotes were used for transcriptome profiling and downstream bioinformatic analysis to determine fibrotic-associated genes regulated early during infection process (0 to 120 minutes). The identification of early molecular host responses to T. cruzi infection can be exploited to delineate important molecular signatures that can be used for the classification of Chagasic patients at risk of developing heart disease. Our results show distinct gene network architecture with multiple gene networks modulated by the parasite with an incline towards progression to a fibrogenic phenotype. Early during infection, T. cruzi significantly upregulated transcription factors including activator protein 1 (AP1) transcription factor network components (including FOSB, FOS and JUNB), early growth response proteins 1 and 3 (EGR1, EGR3), and cytokines/chemokines (IL5, IL6, IL13, CCL11), which have all been implicated in the onset of fibrosis. The changes in our selected genes of interest did not all start at the same time point. The transcriptome microarray data, validated by quantitative Real-Time PCR, was also confirmed by immunoblotting and customized Enzyme Linked Immunosorbent Assays (ELISA) array showing significant increases in the protein expression levels of fibrogenic EGR1, SNAI1 and IL 6. Furthermore, phosphorylated SMAD2/3 which induces a fibrogenic phenotype is also upregulated accompanied by an increased nuclear translocation of JunB. Pathway analysis of the validated genes and phospho-proteins regulated by the parasite provides the very early fibrotic interactome operating when T. cruzi comes in contact with PHCM. The interactome architecture shows that the parasite induces both TGF-β dependent and independent fibrotic pathways, providing an early molecular foundation for Chagasic cardiomyopathy. Examining the very early molecular events of T. cruzi cellular infection may provide disease biomarkers which will aid clinicians in patient assessment and identification of patient subpopulation at risk of developing Chagasic cardiomyopathy., Author Summary About 30% of Trypanosoma cruzi infected individuals will develop Chagas heart disease cardiomyopathy which is similar to other cardiomyopathies. T. cruzi induced heart disease can lead to cardiac arrhythmias, heart failure and death. These cardiac disorders can be caused by several host-parasite interaction factors that lead to myocardial inflammation which remains poorly understood from the initial phase of infection. In this study, we challenged primary human cardiomyocyte (PHCM) with T. cruzi trypomastigotes and evaluated changes in gene and protein expression profile occurring early during the process of cellular infection. Paying attention to genes that have the potential of tilting the PHCM towards a fibrogenic phenotype, we observed that the parasite upregulates the expression of several transcription factors and cytokines/chemokines that have been suggested to be implicated in the development of fibrogenic responses. The validated microarray data were confirmed at the protein level where we demonstrated an increase in the protein expression of EGR1, SNAI1, JunB and IL6. Pathway analyses of our results show that early during T. cruzi infection, the parasite induces multiple factors that are profibrotic in PHCM. The operating fibrotic interactome presented in our work advances our understanding of the initiation of cardiovascular pathology in Chagas disease patients.

Acinetobacter baumannii multidrug-resistant strain MMC4 was isolated from a bronchoalveolar lavage fluid sample from a patient in Nashville, TN, USA. Here, we report a draft genome sequence with a size of 3,985,367 bp, an average G+C content of 39.8%, and 3,863 predicted protein-coding sequences.

Background Over the past decade, pathway and gene-set enrichment analysis has evolved into the study of high-throughput functional genomics. Owing to poorly annotated and incomplete pathway data, researchers have begun to combine pathway and gene-set enrichment analysis as well as network module-based approaches to identify crucial relationships between different molecular mechanisms. Methods To meet the new challenge of molecular phenotype discovery, in this work, we have developed an integrated online database, the P athway A nd G ene E nrichment D atabase (PAGED), to enable comprehensive searches for disease-specific pathways, gene signatures, microRNA targets, and network modules by integrating gene-set-based prior knowledge as molecular patterns from multiple levels: the genome, transcriptome, post-transcriptome, and proteome. Results The online database we developed, PAGED http://bio.informatics.iupui.edu/PAGED is by far the most comprehensive public compilation of gene sets. In its current release, PAGED contains a total of 25,242 gene sets, 61,413 genes, 20 organisms, and 1,275,560 records from five major categories. Beyond its size, the advantage of PAGED lies in the explorations of relationships between gene sets as gene-set association networks (GSANs). Using colorectal cancer expression data analysis as a case study, we demonstrate how to query this database resource to discover crucial pathways, gene signatures, and gene network modules specific to colorectal cancer functional genomics. Conclusions This integrated online database lays a foundation for developing tools beyond third-generation pathway analysis approaches on for discovering molecular phenotypes, especially for disease-associated pathway/gene-set enrichment analysis.

Background Breast cancer is the second most lethal cancer in women. Further, death rates for African American women are the highest for any racial/ethnic group. Hormone receptor status is one of the major prognostic factors and a determinant of treatment options for breast cancer, thus suggesting the importance of molecular level characterization for precision treatments. In this study, we have identified transcriptome level differences correlating to receptor specific molecular subtypes of breast cancer in African American women.