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The contributions of mitochondria to cancer have been recognized for decades. However, the focus on the metabolic role of mitochondria and the diminutive size of the mitochondrial genome compared to the nuclear genome have hindered discovery of the roles of mitochondrial genetics in cancer. This review summarizes recent data demonstrating the contributions of mitochondrial DNA (mtDNA) copy-number variants (CNVs), somatic mutations, and germline polymorphisms to cancer initiation, progression, and metastasis. The goal is to summarize accumulating data to establish a framework for exploring the contributions of mtDNA to neoplasia and metastasis.

We herein provide a brief review of the trajectory that the field of short RNA research followed in the last 25 years. We place emphasis on the unexpected discoveries and the ramifications of these discoveries for the field, as well as offer some thoughts about what the next 25 years may bring. Arguably, the uncovered dependence of different types of short RNAs on individual attributes such as a person's sex, population origin, race, and on tissue type, tissue state, and disease was most unexpected. This dependence has important ramifications in that it will provide a boost to our understanding of the molecular mechanisms of health disparities as well as pave the way for novel approaches to designing improved and personalized diagnostics and therapeutics.

Introduction Parkinson's Disease (PD) is diagnosed clinically. Reliable non-invasive PD biomarkers are actively sought. Transfer RNAs produce short non-coding RNAs, the tRNA-derived fragments (tRF). tRF have been shown to play diverse roles, including in amyotrophic lateral sclerosis, and the response to ischemic stroke. Rich tRF populations are being reported in biofluids. We explored the possibility that tRF can serve as non-invasive biomarkers for PD. Methods We collected existing RNA-seq samples and re-analyzed a total of 254 legacy datasets from 3 previous studies, from male and female PD patients and controls that belong to three categories: prefrontal cortex samples from 29 patients and 33 controls; cerebrospinal fluid (CSF) samples from 63 patients and 64 controls; and, serum samples from 34 patients and 31 controls. First, we identified tRF exhaustively and deterministically in every dataset. Second, we determined tRF that are differentially abundant (DA) between PD and control samples, using uncorrected t-tests. Lastly, we assessed all the DA tRF from the previous step with Partial Least Squares – Discriminant Analysis (PLS-DA) to stringently sub-select tRF that can distinguish PD patients from controls. Results We show that PLS-DA identified tRF from prefrontal cortex, CSF, and serum that can distinguish PD patients from controls. A handful of identified tRF were previously investigated in neurological contexts. Signatures built from relatively few tRF suffice to distinguish PD from control in each category of samples with high sensitivity (89-100%) and specificity (79-98%). Conclusion: tRF-based signatures are promising candidates that warrant further evaluation as non-invasive PD biomarkers.

SummaryRecent studies have reported the identification of piwi-associated RNAs (piRNAs) in Drosophila somatic cells. Interestingly, these piRNAs derive from the 3′ untranslated regions of a subset of transcribed protein-coding genes and, experimentation suggests, might control the expression of other protein-coding transcripts. Studies of additional organisms support the new pathway's presence across animals.

Summary We performed integrated genomic, transcriptomic, and proteomic profiling of 150 pancreatic ductal adenocarcinoma (PDAC) specimens, including samples with characteristic low neoplastic cellularity. Deep whole-exome sequencing revealed recurrent somatic mutations in KRAS , TP5 3, CDKN2A , SMAD4 , RNF43 , ARID1A , TGFβR2 , GNAS , RREB1 , and PBRM1 . KRAS wild-type tumors harbored alterations in other oncogenic drivers, including GNAS , BRAF , CTNNB1 , and additional RAS pathway genes. A subset of tumors harbored multiple KRAS mutations, with some showing evidence of biallelic mutations. Protein profiling identified a favorable prognosis subset with low epithelial-mesenchymal transition and high MTOR pathway scores. Associations of non-coding RNAs with tumor-specific mRNA subtypes were also identified. Our integrated multi-platform analysis reveals a complex molecular landscape of PDAC and provides a roadmap for precision medicine.

The open reading frames of human cytomegalovirus (human herpesvirus-5, HHV5) encode some 213 unique proteins with mostly unknown functions. Using the threading program, ProCeryon, we calculated possible matches between the amino acid sequences of these proteins and the Protein Data Bank library of three-dimensional structures. Thirty-six proteins were fully identified in terms of their structure and, often, function; 65 proteins were recognized as members of narrow structural/functional families (e.g. DNA-binding factors, cytokines, enzymes, signaling particles, cell surface receptors etc.); and 87 proteins were assigned to broad structural classes (e.g. all-beta, 3-layer-alphabetaalpha, multidomain, etc.). Genes encoding proteins with similar folds, or containing identical structural traits (extreme sequence length, runs of unstructured (Pro and/or Gly-rich) residues, transmembrane segments, etc.) often formed tandem clusters throughout the genome. In the course of this work, benchmarks on about 20 known folds were used to optimize adjustable parameters of threading calculations, i.e. gap penalty weights used in sequence/structure alignments; new scores obtained as simple combinations of existing scoring functions; and number of threading runs conducive to meaningful results. An introduction of summed, per-residue-normalized scores has been essential for discovery of subdomains (EGF-like, SH2, SH3) in longer protein sequences, such as the eight "open sandwich" cytokine domains, 60-70 amino acids long and having the 3beta1alpha fold with one or two disulfide bridges, present in otherwise unrelated proteins.

In "all alpha-fold" transmembrane proteins, including ion channels, G-protein-coupled receptors (GPCRs), bacterial rhodopsins and photosynthetic reaction centers, relatively long alpha-helices, straight, curved or kinked, pack into compact elliptical or circular domains. Using both existing and newly developed tools to analyze transmembrane segments of all available membrane protein three-dimensional structures, including that very recently elucidated for the GPCR, rhodopsin, we report here the finding of frequent non-alpha-helical components, i.e. 3(10)-helices ("tight turns"), pi-helices ("wide turns") and intrahelical kinks (often due to residues other than proline). Often, diverse helical types and kinks concatenate over long segments and produce complex inclinations of helical axis, and/or diverse frame shifts in the "canonical", alpha-helical side-chain pattern. Marked differences in transmembrane architecture exist even between seemingly structurally related proteins, such as bacteriorhodopsin and rhodopsin. Deconvolution of these non-canonical features into their composite elements is essential for understanding the pleiotropy of polytopic protein structure and function, and must be considered in developing valid macromolecular models.

In the past few years, pattern discovery has been emerging as a generic tool of choice for tackling problems from the computational biology domain. In this presentation, and after defining the problem in its generality, we review some of the algorithms that have appeared in the literature and describe several applications of pattern discovery to problems from computational biology.